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首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-srw.117ACL Anthology · ACL 2026

Mind the Gap: Multilingual Divide in LLM Bias Detection and Reasoning

Medha Hira, Prachi Goyal, Raj Maheshwari, Arnav Goel

原文摘要

Large Language Models (LLMs) are increasingly deployed in multilingual settings, yet most bias evaluation remains English-centric and overlooks how bias manifests within reasoning. We present a systematic study of social bias in both predictions and chain-of-thought reasoning across English, Dutch, Spanish, and Turkish using the MBBQ benchmark. We evaluate instruction-tuned, CoT-prompted, and reasoning-native models under supervised fine-tuning and preference optimization, using accuracy, F1, bias metrics, and a novel reasoning-level language drift measure. We find that (1) bias varies substantially across languages, with consistent degradation in non-English settings, (2) reasoning traces often introduce additional stereotype-driven signals beyond final outputs, and (3) English-trained debiasing methods fail to generalize reliably, with preference optimization introducing cross-lingual trade-offs. We further show that performance gains in multilingual settings are frequently driven by implicit reliance on English-centric reasoning, revealed through increased language drift. Together, our results demonstrate that multilingual fairness cannot be inferred from English performance and requires reasoning-aware, language-specific evaluation and alignment.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-srw.119ACL Anthology · ACL 2026

Multi-Constraint State Tracking with Negation: A Diagnostic Benchmark for LLM World Modeling

Ayan Sar University of Petroleum and Energy Studies, Pranav Singh Puri, Sumit Aich, Anurag Kaushish, Tanupriya Choudhury, Ajith Abraham Bennett University

原文摘要

Large Language Models (LLMs) achieve strong performance on a wide range of reasoning benchmarks, yet it remains unclear whether they can reliably maintain and update internal representations of an evolving world described in natural language. In particular, existing evaluations inadequately probe state tracking under multiple interacting constraints and largely overlook the role of negated actions, despite their ubiquity in real-world language. We address this gap by introducing MCST, a diagnostic benchmark for multi-constraint state tracking that evaluates an LLM’s ability to maintain consistent world models across sequences of actions involving inventory changes, spatial movement, temporal ordering, and systematic negation. MCST comprises 100,847 questions spanning 12 real-world domains, with five calibrated difficulty levels, nine question types, and controlled integration of negated actions. The benchmark further incorporates culturally diverse entity names to enable analysis of cross-cultural robustness. We evaluate 14 SOTA LLMs across multiple model families using a unified evaluation protocol. Our results reveal substantial limitations: even the strongest models exhibit sharp performance degradation as difficulty increases, with accuracy dropping below 35% at the highest level. Most notably, we identify negation as a dominant failure mode, causing accuracy reductions of 23-32\% across models. We release MCST and the full evaluation framework to support future research on state tracking and reasoning in language models and is available at GitHub.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-srw.12ACL Anthology · ACL 2026

Semantic Contrastive Adaptation for Multimodal Figurative Language Understanding

Ayaan Siddiqui

原文摘要

Understanding idiomatic and figurative language in images remains a fundamental challenge for vision–language models, as it requires reasoning beyond literal image–text alignment. Although large pretrained models such as CLIP and BLIP-2 perform well on literal recognition, they consistently fail on multimodal figurative benchmarks, often favoring visually salient but semantically literal interpretations. We show that this failure arises from a systematic literal alignment bias rather than limited model capacity. Motivated by this observation, we reformulate multimodal figurative understanding as a contrastive semantic deviation problem, where figurative images must be distinguished from visually plausible literal alternatives. We introduce a parameter-efficient adaptation of CLIP using Low-Rank Adaptation (LoRA) with hard literal negative mining, achieving targeted reshaping of multimodal representations without full fine-tuning. Experiments on the IRFL benchmark across idioms, metaphors, and similes demonstrate substantial improvements over zero-shot CLIP, BLIP- 2, ensemble-based, and knowledge-augmented baselines. Finally, we introduce FIGMENT, a multilingual figurative grounding evaluation spanning five idiom-rich languages, and show that the adapted model generalizes across languages despite being trained exclusively on English supervision.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-srw.120ACL Anthology · ACL 2026

Learning Shortcut Models for Efficient Recursive Reasoning

Shiv Shankar IIT Bombay and University of Massachusetts

原文摘要

Recursive models that progressively refine latent representations have demonstrated strong performance on a variety of reasoning tasks. However, these models only control whether and when to stop early, not how computation is distributed. In this work, we introduce shortcut reasoning, a framework for distilling recursive latent reasoning into a multiscale jump model that enables flexible test-time compute. We reinterpret recursive reasoning as a latent-time dynamical process and train a student model to predict the effect of multiple reasoning steps at once. To ensure robustness, we augment shortcut transitions with a repair mechanism, where a denoising variant of the base model projects latent states back onto a valid reasoning manifold. We further introduce stepwise improvement supervision, encouraging each shortcut step to increase the likelihood of the correct answer. Experiments on ARC-AGI show that our approach achieves competitive accuracy compared to recursive baselines while requiring fewer sequential updates.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-srw.121ACL Anthology · ACL 2026

The Confident Liar: Diagnosing Multi-Agent Debate with Log-Probabilities and LLM-as-Judge

Ali Keramati University of California, Irvine, Justin Cheok, Jacob Horne, Mark Warschauer University of California, Irvine

原文摘要

Multi-agent debate systems are typically evaluated only on whether thefinal answer is correct, overlooking the quality of the intermediatereasoning that debate is designed to produce. This paper studies therelationship between three signals in multi-agent debate: token-levellog-probability distributions over reasoning tokens, LLM-as-judge rubricscores assigned to those tokens, and final task accuracy. We examinewhether internal confidence signals predict externally evaluated reasoningquality, and whether either signal aligns with task correctness, acrossthree domains: rubric-based scoring, mathematical reasoning, and factualquestion answering. Our framework pairs a two-agent debate architecture—a Constructor and an Auditor—with anLLM-as-judge that scores each agent’s reasoning along instructionfollowing, justification quality, and evidence grounding, together with acritical-failure flag. Experiments in the rubric-scoring domain reveal aconsistent four-phase confidence trajectory and a substantial roleasymmetry: confidence aligns with judged reasoning quality roughly twiceas strongly for the Constructor as for the Auditor, and confidence-based detection ofcritical reasoning failures is markedly more reliable for the Constructor(AUROC 0.804) than for the Auditor (0.634). These findings motivate thebroader cross-domain investigation proposed in this paper.

cs.CL · cs.MA

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-srw.122ACL Anthology · ACL 2026

Convergent Demographic Utility Hierarchies: Geometry of Intersectional Values in LLMs

Pravish Sainath Montreal Institute for Learning Algorithms, University of Montreal, University of Montreal

原文摘要

Recent work has shown that LLMs develop internally coherent utility functions that emerge with scale, yet whether these value systemsencode systematic demographic hierarchies remains unexplored. We elicit pairwise preferences across 15 intersectional demographic groups (defined by race, gender, and their combinations) and 8 policy domains on three 7–8B instruction-tuned LLMs, fitting Thurstonian utility models to the resulting preference matrices. All three models converge on a compensatory hierarchy that invertsreal-world structural advantage, consistently ranking marginalized groups, the highest and dominant groups are lowest. Intersectional utilities do not combine additively: single-axis audits that measure gender and race gaps independently overestimate the most extreme intersectional gap by 26- 40% in our experiments. Geometrically, we identify a linear direction in the representation space that predicts the full utility hierarchy from neutral sentences alone, and show that this direction is substantially aligned with gender encoding but not with race encoding. Orthogonalization reveals that gender separation in representations is not fully explained by utility encoding. The hierarchy is already present in base (pre-alignment) models and is amplified several-fold by instruction tuning, suggesting it originates in pre-training data rather than alignment procedures.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-srw.123ACL Anthology · ACL 2026

CRL-Prompt: Contrastive and Reinforcement Learning for Soft Prompt Tuning for Text Classification

Danila Lapokin, Andrey Savchenko HSE University

原文摘要

Prompt choice is crucial in adapting language models to text classification tasks, particularly under low-resource conditions. Manual prompt engineering is time-consuming, non-scalable, and brittle, while current auto-prompting techniques are still far from maturity. This paper presents a two-stage method for prompt learning of frozen language models, CRL-Prompt, based on soft prompt initialization followed by contrastive and reinforcement-based refinement. An experimental study demonstrates that our approach achieves consistent improvements in accuracy over baseline prompt tuning strategies, with gains of up to 2.2% while training fewer than 0.25% of model parameters.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-srw.124ACL Anthology · ACL 2026

Optimizing Packing and Shuffling Strategies for Enhanced Performance in Generative Language Models

Yanbing Chen, Ruilin Wang, Zihao Yang Northeastern University, Lavender Yao Jiang, Eric Karl Oermann New York University

原文摘要

Packing and shuffling tokens is a common practice in training auto-regressive language models to prevent overfitting and improve efficiency. Documents are typically concatenated to chunks of maximum sequence length (MSL) and shuffled in chunks of tokens (atom-size chunk), possibly breaking context within documents. An alternative approach is padding, which only includes one document per chunk. To optimize both packing strategies (concatenation vs padding), we explored the optimal atom size for shuffling and compared performance and efficiency. We found that in the most common setup (where average document length is greater than MSL), matching atom size to MSL yields the lowest perplexity, controlling for dataset. Also, padding yields lower final perplexity than concatenation at the cost of lower efficiency. This trade-off informs the choice of shuffling and packing methods in training LMs.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-srw.125ACL Anthology · ACL 2026

LLM as a Meta-Judge: Synthetic Data for NLP Evaluation Metric Validation

Lukáš Eigler Charles University Prague, Jindřich Libovický Charles University Prague, David Hurych Valeo.ai

原文摘要

Validating evaluation metrics for NLG typically relies on expensive and time-consuming human annotations, which predominantly exists only for English datasets. We propose LLM as a Meta-Judge, a scalable framework that utilizes LLMs to generate synthetic evaluation datasets via controlled semantic degradation of real data, replacing human judgment. We validate our approach using meta-correlation, measuring the alignment between metric rankings derived from synthetic data and those from standard human benchmarks. Experiments across Machine Translation, Question Answering, and Summarization demonstrate that synthetic validation serves as a reliable proxy for human judgment, achieving meta-correlations exceeding 0.9 in multilingual QA and proves to be a viable alternative where human judgments are unavailable or too expensive to obtain. Our code and data are publicly available at https://github.com/eiglerl/meta-judge.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-srw.126ACL Anthology · ACL 2026

Continuous Context Sampling Allows Extending Diversity Boundaries of Large Language Models

Mateusz Bystroński, Doheon Han University of Notre Dame, Nitesh V. Chawla University of Notre Dame, Tomasz Jan Kajdanowicz Wroclaw University of Science and Technology

原文摘要

Starting from the observation that conditioning a poetry-writing prompt with a pancake recipe leads an LLM to produce a coherent poem incorporating pancake-related content and, more broadly, that such contexts arrange themselves into a structured semantic vector space, we argue that this renders the space explorable. By sampling it and using the resulting continuous representations to condition an LLM’s generation distribution, we can systematically expand the model’s reachable semantic range.We introduce a framework that requires no modification of LLM parameters and operationalizes this idea by constructing a conditioning distribution from a small set of diverse anchor generations. This distribution conditions LLM’s generation via an xRAG-style projector.Our experiments demonstrate that this manifold-based conditioning substantially increases generative diversity, with direct benefits for enhancing divergent thinking, a core facet of creativity, in language models.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-srw.127ACL Anthology · ACL 2026

One Task Vector is not Enough: A Large-Scale Study for In-Context Learning

Pavel Tikhonov, Ivan Oseledets Institute of Numerical Mathematics, Elena Tutubalina

原文摘要

In-context learning (ICL) enables Large Language Models (LLMs) to adapt to new tasks using few examples, with task vectors, defined as specific hidden state activations hypothesized to encode task information. Existing studies are limited by small-scale benchmarks, restricting comprehensive analysis. We introduce QᴜɪᴛᴇAFᴇᴡ, a novel dataset of 3,096 diverse few-shot tasks, each with 30 input-output pairs derived from the Alpaca dataset. Experiments with Llama-3-8B on QᴜɪᴛᴇAFᴇᴡ reveal: (1) task vector performance peaks at an intermediate layer (e.g., 15th), (2) effectiveness varies significantly by task type, and (3) complex tasks rely on multiple, subtask-specific vectors rather than a single vector, suggesting distributed task knowledge representation.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-srw.13ACL Anthology · ACL 2026

Think Less, Code Better: Probing When Chain-of-Thought Hurts and How to Route Around It

Rajarshi Ghoshal, Debadri Basak, Salma E. Abdelhalim, Pratibha K. Arora Georgia Institute of Technology

原文摘要

Chain-of-Thought (CoT) prompting is the dominant strategy for eliciting step-by-step reasoning in large language models, but its effect on code generation is poorly understood. We present a controlled 2×2 study of Qwen2.5-Coder-1.5B and DeepSeek-Coder-1.3B (each in base and instruction-tuned variants) on HumanEval, MBPP, and LiveCodeBench, plus scale-validation runs on Qwen2.5-Coder at 7B and 14B and a preliminary evaluation of CodeLlama-7B. We find that instruction tuning reverses CoT’s effect on small Qwen models: CoT improves the 1.5B base (+13.4pp, p<0.001) but significantly degrades the 1.5B instruct variant (-15.2pp, p<0.001). The reversal is sharply scale-bounded — it disappears at 7B (-0.6pp) and goes slightly positive at 14B (+2.4pp) — while CoT’s positive effect on base models grows monotonically with scale (+13.4 → +28.7 pp). DeepSeek-Coder-1.3B is insensitive regardless of regime. A direct token-count and truncation analysis shows the mechanism: at 1.5B, CoT inflates Qwen Instruct’s mean output length by 112 tokens and pushes 7.6× more generations into truncation, where Pass@1 is 0%; at 14B, the same prefix produces complete code well within budget. Layer-wise probing shows all four small models encode prompt type by Layer 1–4 (>90% accuracy) — universally, whether CoT helps or hurts — demonstrating that representation does not determine interpretation: the same internal signal drives divergent downstream behavior depending on training regime and capacity. Building on these mechanistic findings, we develop a probe-guided style router that, when trained per model on a labeled training split, selects among 12 prompt styles via a single 84 ms forward pass; it is statistically indistinguishable from the best fixed style in 7/8 settings and significantly outperforms CoT where CoT is most harmful (p=0.012, h=+0.40). Our results argue against applying CoT blindly to small instruct code models: its effect depends on architecture, training regime, and scale in ways that are mechanistically detectable from early-layer activations.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-srw.15ACL Anthology · ACL 2026

Probing Functional Correctness in Diffusion Language Models

Guan-Ming Chiu, Jeng-Yue Liu

原文摘要

Diffusion language models generate text by iteratively denoising all tokens in parallel, but when and where their hidden states encode whether the output will be functionally correct remains unknown.We present the first probing study of DLM internals, training linear classifiers on hidden states to predict functional correctness.Across two models (LLaDA-8B, Dream-7B) and four tasks, we find that DLMs uniquely accumulate correctness signal across denoising steps (AUC gains of 0.08–0.11 on reasoning tasks), absent in single-pass AR decoding. However, step-0 signal reflects prompt difficulty rather than diffusion-specific computation. Signal emergence is task-dependent: structural tasks show flat profiles while reasoning tasks show gradual buildup. The two models exhibit distinct layer dynamics, with LLaDA concentrating signal in upper layers while Dream redistributes toward lower layers. We further show that probe confidence can identify likely failures, enabling selective generation that avoids 36–98% of wasted compute.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-srw.17ACL Anthology · ACL 2026

Thesis Proposal: On the Granularity-Robustness Trade-off in Text-Derived Knowledge Graphs

Surawat Pralomram Mahidol University

原文摘要

Retrieval-augmented generation (RAG) based on dense embeddings has become a dominant paradigm for text retrieval. However, many real-world applications require attribute-specific querying, where explicit values or properties must be extracted from text (e.g., symptoms in clinical notes or dosage values in medical reports). Dense retrieval handles paraphrastic variation well but often entangles multiple attributes within a single embedding, making value extraction difficult. Knowledge graphs (KGs), in contrast, support explicit attribute access but are brittle under linguistic and structural variation, leading to low recall.This thesis proposal aims to investigate the representational trade-off underlying these approaches. We study knowledge graph representations from an information-theoretic and optimal coding perspective, focusing on the tension between fine-grained factorization and compact canonicalization of concepts. Building on this perspective, we propose a query-driven framework for constructing and retrieving knowledge graphs from text, aiming to combine the robustness of dense retrieval with the explicit queryability of symbolic representations.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-srw.18ACL Anthology · ACL 2026

TokLens: A Multilingual Lens on Tokenizer Quality for LLMs

Guan-Ming Chiu

原文摘要

We introduce TokLens, an open-source toolkit for evaluating tokenizer quality across languages using six intrinsic metrics: fertility, characters per token, compression ratio, normalized sequence length, single-token retention rate, and cross-lingual parity. We evaluate 24 tokenizers from major LLM families across 15 typologically diverse languages and correlate these metrics with downstream performance. Our analysis reveals stark disparities: GPT-2 produces 56x more tokens per word in Japanese than in English, while newer tokenizers like Qwen2.5 and Gemma-2 reduce this gap to under 4x. No intrinsic metric predicts English benchmark performance after controlling for model size. However, on multilingual benchmarks (MMLU-ProX), linear mixed-effects models show that tokenizer metrics significantly predict per-language performance (STRR: \beta = +5.7, z = 18.5, p < 0.001). A controlled experiment on the Qwen2.5 family further shows that languages with higher single-token retention rate exhibit steeper scaling slopes (\rho = 0.91, p < 0.001). These results indicate that tokenizer quality is significantly associated with multilingual LLM performance, though the evidence remains correlational and partially confounded with pretraining data composition.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-srw.19ACL Anthology · ACL 2026

Phase Transitions in Affective Meaning Divergence: The Hidden Drift Before the Break

Napassorn Litchiowong

原文摘要

One partner says “Fine” meaning resolution; the other hears surrender. The word is shared; the affective uptake is not. We formalize this as affective meaning divergence (AMD), the total-variation distance between interlocutors’ anchor-conditioned affect distributions. Building on speech-act theory, common-ground accumulation, and entropy-regularized game theory, we derive a logit best-response map whose dynamics undergo a saddle-node bifurcation: when \beta\alpha > 4, a monotone increase in AMD-driven load produces an abrupt, hysteretic collapse of repair coordination. On Conversations Gone Awry (CGA-Wiki; N=652), derailing conversations exhibit critical-slowing-down (CSD) signatures across multiple levels: lexical divergence variance (p<0.001, d=0.36), AMD variance (p=0.001, d=0.26), and dialog-act repair variance (p=0.016, d=0.20), all significant after correction and stronger than toxicity and sentiment baselines. AMD provides a distinct temporal signature, with retrospectively measured variance peaking at the bifurcation point while toxicity variance peaks earlier, and is the only indicator grounded in the theoretical framework. Boundary-condition analysis on CGA-CMV (N=1,169) yields mixed but directionally consistent evidence.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-srw.2ACL Anthology · ACL 2026

Claim Verification in the Age of Large Language Models: A Survey

Alphaeus Dmonte, Roland R Oruche, Marcos Zampieri George Mason University, Prasad Calyam, Isabelle Augenstein University of Copenhagen

原文摘要

The large and ever-increasing amount of data available on the Internet, coupled with the laborious task of manual claim and fact verification, has sparked interest in the development of automated claim verification systems. Several deep learning and transformer-based models have been proposed for this task over the years. With the introduction of Large Language Models (LLMs) and their superior performance in several NLP tasks, we have seen a surge of LLM-based approaches to claim verification along with the use of novel methods such as Retrieval Augmented Generation (RAG). In this survey, we present a comprehensive account of recent claim verification frameworks using LLMs. We describe the different components of the claim verification pipeline used in these frameworks in detail, including common approaches to retrieval, prompting, and fine-tuning. Finally, we describe publicly available English datasets for this task.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-srw.20ACL Anthology · ACL 2026

Sycophantic Anchors: Localizing and Quantifying User Agreement in Reasoning Models

Jacek Duszenko, Przemyslaw Kazienko Wroclaw University of Science and Technology, Jan Kocon Wroclaw University of Science and Technology

原文摘要

Reasoning models frequently agree with incorrect user suggestions - a behavior known as sycophancy. However, it is unclear where in the reasoning trace this agreement originates and how strong the commitment is. We introduce sycophantic anchors - sentences identified via counterfactual analysis that commit models to user agreement. Across four reasoning models spanning three architecture families (Llama, Qwen, Falcon-hybrid) and 1.5B - 8B parameters, we analyze over 200,000 counterfactual rollouts and show that linear probes reliably detect sycophantic anchors (74 - 85% balanced accuracy), outperforming text-only baselines at high commitment levels -confirming they capture internal states beyond surface vocabulary. Regressors further predict commitment strength from activations (R^2 up to 0.74). We observe a consistent asymmetry: sycophancy leaves a stronger mechanistic footprint than correct reasoning. We also find that sycophancy builds gradually during generation rather than being determined by the prompt. These findings enable sentence-level detection and quantification of model misalignment mid-inference.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-srw.21ACL Anthology · ACL 2026

NEAT-IR: Neural Explainable Analysis Tool for Information Retrieval

Lev Sukherman Worcester Polytechnic Institute, Artem Frenk Worcester Polytechnic Institute, Nina Klimenkova Worcester Polytechnic Institute, Connor Jason Worcester Polytechnic Institute

原文摘要

Neural IR models achieve strong performance but remain difficult to interpret. We present NEAT-IR, a black-box analysis framework that explains ColBERT’s ranking behavior using 26 classical IR features (BM25, TF-IDF, IDF measures, positional signals). We analyze ColBERT through two complementary lenses: regression (predicting exact scores) and learning-to-rank (predicting relative order), evaluated on MS MARCO (48,250 query-passage pairs). Our key finding is a score-rank gap: classical features preserve ColBERT’s rankings nearly perfectly (NDCG@5 \approx 0.99) yet explain only R^2 \approx 0.28 of score variance. Feature attribution reveals that regression and ranking models rely on distinct feature subsets: query-level IDF signals dominate score prediction, while document-matching features (BM25, cosine TF-IDF) drive ranking preservation. These findings suggest that ColBERT’s ordinal behavior on MS MARCO is largely recoverable from classical signals, while neural contributions primarily affect score magnitude. NEAT-IR enables practitioners to diagnose when neural rankers deviate from classical patterns, supporting interpretable model auditing and informed hybrid pipeline design.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-srw.22ACL Anthology · ACL 2026

BanglaSocialBench: A Benchmark for Evaluating Sociopragmatic and Cultural Alignment of LLMs in Bangladeshi Social Interaction

Tanvir Ahmed Sijan, S. M. Golam Rifat Rajshahi University of Engineering & Technology, Pankaj Partha Bangladesh University of Engineering and Technology, Md. Tanjeed Islam Jahangirnagar University, Md Musfique Anwar

原文摘要

Large Language Models have demonstrated strong multilingual fluency, yet fluency alone does not guarantee socially appropriate language use. In high-context languages, communicative competence requires sensitivity to social hierarchy, relational roles, and interactional norms that are encoded directly in everyday language. Bangla exemplifies this challenge through its three-tiered pronominal system, kinship-based addressing, and culturally embedded social customs. We introduce BanglaSocialBench, the first benchmark designed to evaluate sociopragmatic competence in Bangla through context-dependent language use rather than factual recall. The benchmark spans three domains: Bangla Address Terms, Kinship Reasoning, and Social Customs, comprising 1,719 culturally grounded instances written and verified by native Bangla speakers. We evaluate twelve contemporary LLMs in a zero-shot setting and observe systematic patterns of cultural misalignment. Models frequently default to overly formal address forms, fail to recognize multiple socially acceptable address pronouns, and conflate kinship terminology across religious contexts. Our findings show that sociopragmatic failures are often structured and non-random; for example, inappropriate addressing choices concentrate heavily in downward-hierarchy (Elder\rightarrowYounger) and informal contexts. This reveals persistent limitations in how current LLMs infer and apply culturally appropriate language use in realistic Bangladeshi social interactions.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-srw.23ACL Anthology · ACL 2026

Interpretability of LLM Classifiers via the Rational Inattention Theory with Application to Hate Speech Detection

Yuan Zhao, Ali Abdi New Jersey Institute of Technology

原文摘要

Hate speech detection is essential for maintaining healthy online communities. Large language models (LLMs) perform well on text classification, yet their decision strategies need to be better understood. While post-hoc rationales can justify individual decisions, they substantially increase inference cost and limit scalability in high-throughput settings. As another approach, we propose an extended rational inattention model that parameterizes linguistic noise and information processing cost, providing an interpretable behavioral framework for black-box LLM classifiers. Treating LLMs as rational decision-makers under information constraints allows us to estimate - from the observed classification behavior - the parameters that represent information processing cost and noise sensitivity. As a case study and using a hate-speech dataset spanning multiple noise environments, we evaluate four commercial LLMs and show that the introduced extended rational inattention model predictions closely match the observed performance across different noise levels. We further test the performance under various noise mechanisms and find that the inferred information cost parameters remain consistent while the noise parameters vary with the distortion mechanism. Overall, our introduced framework offers a cost-efficient and quantitative approach to derive interpretable indices of LLM moderation behavior and decisions, without additional rationale generation.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-srw.24ACL Anthology · ACL 2026

The Shape of Vulnerability: How Adversarial Perturbations Reshape the Topology of Language Model Latent Spaces

Angelina Tsai, Shreya Subramanian, Catherine Liu, Kimberly Lopez, Leif Zinn-Brooks, Alexia E. Schulz, Adaku Uchendu MIT Lincoln Laboratory

原文摘要

Adversarial perturbations in the context of large language models (LLMs) are subtle changes added to input data (i.e., images or text) that are designed to alter predictions or outputs of machine learning models. We introduce several novel visualizations using topological data analysis (TDA) (leveraging persistent homology) to characterize how adversarial perturbations act on text inputs, specifically, how sandbagging and code-injection attacksalter the geometric structure of attention heads in transformer models. By computing persistent homology metrics from attention maps across different model architectures (such as BERT, RoBERTa, ELECTRA, DistilGPT, etc.), we find that adversarial inputs alter higher-dimensional topological features (H_{1} loops and H_{2} voids) in ways that distinguish them from clean, non-adversarial inputs.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-srw.25ACL Anthology · ACL 2026

LLM-based Literal Example Generation for Japanese Multiword Expressions

Mio Ohashi, Hajime Kiyama Hitotsubashi University, Zhidong Ling, Mamoru Komachi Hitotsubashi University

原文摘要

We investigate whether large language models (LLMs) can generate literal usage examples for Japanese multiword expressions (MWEs), whose literal readings are structurally low-frequency in available corpora.Prior work on MWEs has largely focused on detecting idiomatic usages in context, leaving literal usages underrepresented particularly for Japanese MWEs whose literal readings are rare and structurally diverse.Because literal readings are rarely attested in corpora, we design a lexicon-grounded setup that uses corpus non-literal usages as contrastive cues for controlled prompting. We evaluate the generated sentences using automatic literalness judgments and human literalness judgments, together with manual inspection.Our results show that providing contrastive non-literal information stabilizes literal generation and improves quality compared with prompts that include only literal information or no hints. In addition, we conduct an LLM-based understanding test that compares model predictions of literal and idiomatic plausibility with human judgments.The results indicate that the model aligns more closely with human judgments for idiomatic interpretations than for literal ones, highlighting the relative difficulty of modeling literal readings of MWEs.The study demonstrates that LLMs can complement existing resources by supplying frequency-independent literal examples and offers a controlled framework for examining contextual meaning understanding of Japanese MWEs.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-srw.26ACL Anthology · ACL 2026

Presentation Slide Translation and Layout Error Correction by LLMs

Futo Kajita, Nobuyori Nishimura, Takehito Utsuro University of Tsukuba, Naoki Muto, Chee Siang Leow University of Yamanashi, Hiromitsu Nishizaki Yamanashi University

原文摘要

We propose a novel approach to translating Japanese slides into English andto correcting their layout errors by utilizing multimodal LLMs with slide images and XML structures.Existing translation tools often suffer from layout errors after translationdue to text expansion during the translation process, causing text to overlap with figures or other items in slides and thereby reducing readability. To overcome this issue, our proposed framework introduces two steps consisting of (i) translating text fragments within the slide, and (ii) correcting layout errors by optimizing layout placement based on visual consistency. In step (ii), we empirically show that few-shot prompts are quite effective in layout error correction. Given that the optimal combination of steps (i) and (ii) varies depending on the slide layout, our method generates eight different layout candidates. Consequently, we introduce a third step that automatically selects the optimal output from these eight candidates.The experimental results showed that the proposed method outperformed baselines and achieved 4.1% layout error rate and over 80% model selection success rate.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-srw.27ACL Anthology · ACL 2026

Constructing a Japanese Rap Lyric Generation Model with GRPO

Hayato Ogawa Waseda University, Daisuke Kawahara Waseda University

原文摘要

Rap is a vocal style rooted in Hip-Hop culture, characterized by producing rhymes in synchrony with a rhythmic beat.This paper proposes a method for generating Japanese rap lyrics with a large language model (LLM) whose rhyming behavior is improved via reinforcement learning.We design a reward function that evaluates end rhymes between two generated bars and apply GRPO, a reinforcement-learning method, to encourage Japanese rhyming without using existing Japanese rap lyrics as training data.Experimental results show that, although output collapse is observed in some cases, GRPO increases the proportion of outputs that receive moderate or high human ratings on rhyme-related criteria.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-srw.28ACL Anthology · ACL 2026

Tracking the Evolution of Foresight Signals in News Data: The Case of the European Electric Vehicle Market

Karine Navasartian Volkswagen Group

原文摘要

To prepare for an uncertain future, organizations must continuously monitor emerging trends and early signals of change. The increasing availability of web-based textual data has boosted natural language processing (NLP) methods in strategic foresight, particularly in the scanning phase. While prior studies have extensively focused on the identification of signals in such data, considerably less attention has been paid to how these signals evolve over time and gain relevance as they become more visible. This study addresses this gap by examining whether tracking the temporal dynamics of signals can improve their assessment for strategic decision-making. Demonstrated on the use case of the European electric vehicle market, we find three dominant signal trajectories and show that burst dynamics tend to surface signal consolidation rather than the early detection of weak signals. The results indicate that foresight research should move beyond static, one-off analyses toward a dynamic temporal perspective capable of identifying signals at earlier stages of emergence.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-srw.3ACL Anthology · ACL 2026

Language Directions in Multilingual LLMs: A Layer-wise Diagnostic Study of Token Alignment and Pretraining Imprint

JaeSeong Kim, Suan Lee Semyung University

原文摘要

We investigate how multilingual representations emerge across depth in large language models.Using a unified probing framework, we analyze six multilingual LLMs across five languages (EN/ES/ZH/FR/DE), decomposing behavior into (i) early-layer dynamics, (ii) linear vs. MLP separability, and (iii) token–language alignment that tracks where vocabulary sharing peaks.Across models, we observe a consistent and substantial early jump: accuracy rises by +73.5 to +80.7 points from L0 to L1 on average, indicating that language-relevant signals become accessible immediately after the embedding layer.Moreover, representations are largely linearly separable: for 5/6 models, the mean gap between MLP and linear probes remains within \pm0.5 points.Token–language alignment further reveals systematic structure, with peak vocabulary mass exceeding 48% in some models and substantial variation in the depth of peak sharing.These findings provide a compact, cross-model characterization of how multilingual information is organized across depth and introduce simple alignment metrics that complement accuracy-based evaluation.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-srw.30ACL Anthology · ACL 2026

Debiasing Logical Fallacy Detection for Real-World Robustness via Counterfactually Augmented Data

Navyansh Singh

原文摘要

Logical fallacy detection models frequentlyover-flag valid reasoning due to reliance onsurface-level spurious correlations. We in-troduce 703 LLM-generated CounterfactuallyAugmented Data (CAD) pairs—minimally dif-ferentiated valid and fallacious arguments—todebias models through targeted augmentation.Fine-tuning DeBERTa-v3-large on CoCoLoFaaugmented with these pairs yields marginalin-distribution improvement (+0.4% F1) butsubstantial out-of-distribution robustness: 58%relative reduction in false positive rate (64%→ 26.7%) on a 300-sample Reddit-sourcedevaluation set. While recent LLMs (Llama-3.1-8B, Llama-3.3-70B) achieve high perfor-mance under optimized prompts (F1 90–94%),they degrade severely under simple human-like prompts (F1 63–72%, FPR 54–74%).Our lightweight, prompt-invariant approachachieves competitive robustness (F1 85.9%,FPR 26.7%) across all prompting regimes with-out prompt engineering, making it stable forproduction deployment with unpredictable userinput. The dataset and model are publicly re-leased.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-srw.31ACL Anthology · ACL 2026

Bring Linguistics Back to Cryptanalysis: Using Attestation to Break the Advanced Encryption Standard

Madeline Boese University of Florida

原文摘要

The Advanced Encryption Standard (AES) is currently considered the preferred standard of encryption for secure messaging by the National Institute of Standards and Technology. While its predecessor, the Data Encryption Standard (DES), can be analyzed in mere seconds with modern cryptanalysis algorithms, those same algorithms are unfeasible for cryptanalysis of AES. This is primarily because the key, or the list of values used during encryption, was increased from a length of 56 bits to 128 bits. The list of all possible combinations, also called the probability space, is tens of magnitudes larger in AES than DES. Current DES cryptanalysis methods use mathematical methods to find the most statistically likely approximate key in the entire probability space. The increased probability space in AES is too large to find an approximation using these formulas. However, these methods operate under the assumption that every key is possible. While this is mathematically true, when looked at from the lens of a linguist, many of these keys create messages that are impossible in language. Linguistic attestation is the documentation that a word, grapheme, sound, or other linguistic feature exists in a language. This thesis proposal presents an algorithm that eliminates AES keys using linguistic attestation. I apply the idea that any grammatical message encrypted by AES will not contain any “unattested” data in its input. The algorithm proposes trimming the probability space by removing keys that are unattested. Once a smaller probability space has been created, any method that searches the probability space for a solution may be applied to find the key from the new, smaller list of filtered keys.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-srw.32ACL Anthology · ACL 2026

Garden Path Recovery in Causal and Masked Language Models

Sanjan Baitalik, Rajashik Datta

原文摘要

Garden-path sentences offer a controlled probe of English incremental sentence processing because they require a reader to revise an initially plausible parse when a later region disambiguates the structure. We present an architecture-aware comparison of garden-path recovery in causal and masked language models using 100 English garden-path/control pairs (200 sentences) spanning three constructions: NP/Z, where a noun phrase is initially read as a direct object but must be reanalyzed as the subject of a zero-complement clause; NP/S, where a noun phrase must be reanalyzed as the subject of an embedded sentence; and MV/RR, where an apparent main verb must be reanalyzed as a reduced relative modifier. Causal models are evaluated with left-to-right word surprisal, whereas masked models are evaluated with pseudo-surprisal derived from masked language model scoring. Beyond the disambiguating word, we analyze cumulative excess surprisal, area-under-curve recovery summaries, and layer-wise hidden-state divergence between each garden-path sentence and its minimally different control. Across the audit-valid model set, causal models show larger within-model disambiguation effects than masked models overall, with the clearest family-level difference on NP/Z constructions. We interpret this difference cautiously because surprisal and pseudo-surprisal are not numerically commensurable across architectures or tokenizers. The results nevertheless show that architecture changes which recovery signals are observable: decoder-only models exhibit sharper online disruption at the point of syntactic revision, while bidirectional encoders appear comparatively buffered at the disambiguator due to right-context access. More broadly, the findings argue that garden-path evaluation should emphasize recovery dynamics, not merely end-state plausibility or task accuracy.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) · 2026 · 正式发表