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首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.132ACL Anthology · ACL 2026

Entity Exchange in the Wild: A Diagnostic Study of LLM Based Real-World Conversational Entity Extraction

Soumya Jain, Ayush Kumar

原文摘要

Entity extraction from spoken customer–agent conversations is increasingly driving automation in contact centers. In these settings, extraction errors can trigger incorrect system actions, including database updates, verification failures, and unintended workflow execution. While prior work has examined the impact of transcription noise and cross-turn reasoning, it has not systematically analyzed how entity-exchange phenomena themselves shape extraction performance.We model conversational entity exchange along three orthogonal axes: Initiation (how an entity becomes relevant in the dialogue), Evolution (how commitment to an entity’s value develops or changes across turns), and Articulation (how the final committed value is expressed in surface form).We evaluate 16 large language models on 6,387 real-world customer–agent conversations spanning 12 entity types across numeric, alphanumeric, temporal, and free-text categories. Performance varies by as much as 50–60% within the same model depending solely on the underlying entity-exchange phenomena. The most severe failures occur when entity values are revised during the interaction and the model must distinguish intermediate mentions from the final committed value. Even in the absence of revision, digit-by-digit and encoded expressions remain persistent sources of error.Error-Aware prompting improves extraction across all three axes, yielding average gains of up to 6.4% across models. Together, this work provides a structured framework for benchmarking entity extraction in real-world deployments and isolating systematic failure modes grounded in conversational structure.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.133ACL Anthology · ACL 2026

Agentic Context Strategies for Multi-Format Document Understanding: When Should Language Models Use Tools?

Mansi Uniyal, Mukul Singh, Ryan Nadel

原文摘要

Large language models face fundamental trade-offs when processing long documents: full context is expensive and may exceed limits, while RAG risks missing relevant information. We evaluate four context strategies across six frontier models on three document formats (Word, Excel, and PowerPoint). Our key finding: agentic tool-augmented approaches dramatically outperform passive strategies, with RAG+Tools achieving 46% accuracy vs 6% for RAG-only. Tool benefits are consistent across formats (+28-40 points) and models. We further show that (1) intelligent routing matters more than iteration count, (2) tools provide unique capability beyond reasoning loops, and (3) forcing active exploration matches providing context proactively. These results suggest tool augmentation is crucial for complex document QA.

cs.CL · cs.AI

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.134ACL Anthology · ACL 2026

MM-BizRAG: Rethinking Multimodal Retrieval-Augmented Generation for General Purpose Enterprise Q&A

Hanoz Bhathena, Parin Rajesh Jhaveri, Rohan Mittal, Prateek Singh, Aymen Kallala, Rachneet Kaur, Yiqiao Jin, Zhen Zeng 等 10 位作者

原文摘要

Recent advances in multimodal retrieval-augmented generation (MM-RAG) have shifted toward minimal parsing, relying on page-level images for producing retriever embeddings and for answer generation. While efficient, this trend often neglects explicit handling of the rich, structured information in complex enterprise documents, instead depending on pre-trained embeddings or vision-language models to implicitly capture such structure. In this work, we take a more direct approach: MM-BizRAG proactively extracts and represents document structure via a document structure-aware split that dynamically routes documents through orientation-specific ingestion pipelines, applying explicit layout-aware parsing for vertically structured documents (e.g., reports) and holistic page-level representations for horizontally structured documents (e.g., slide decks). A unified LLM-driven artifact transformation pipeline with placeholder-based positional alignment preserves natural reading order, while inference-time multimodal assembly decouples retrieval representations from generation context, enabling richer, more grounded answers without any finetuning requirement. Through experiments on a large, heterogeneous enterprise dataset and two public benchmarks (SlideVQA and FinRAGBench-V), MM-BizRAG consistently outperforms state-of-the-art vision-centric baselines by up to 32% points, with especially strong gains on report-style layouts. Furthermore, we introduce FastRAGEval, a single-call LLM Judge metric for fine-grained generative recall that halves RAGChecker’s cost while achieving stronger human alignment.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.135ACL Anthology · ACL 2026

AesX: Enhance Your Images with Stunning Aesthetic Beauty

Yuyan Chen, Zhendong Hou, Lei Xia, Jiahao Li, Zhuolin Ji, Zhixu Li

原文摘要

In the fields of advertising design, artistic creation, and cultural dissemination, there is an increasingly urgent demand for high-quality images that cater to fine-grained aesthetic preferences. Although existing large-scale models can generally meet basic requirements for clarity and alignment with textual elements, they still face significant bottlenecks in achieving precise control and aesthetic optimization. To address this limitation, we propose a set of comprehensive preference indicators across two major dimensions, text-image consistency and aesthetic quality, encompassing multiple criteria ranging from exposure and clarity to visual guidance and innovativeness. Building on these indicators, we have developed a generative framework named AesX to steer the model consistently toward a generation path that more closely aligns with human aesthetic sensibilities. Our experimental findings demonstrate that this approach yields significant improvements in both target recognition accuracy and overall visual aesthetic presentation.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.136ACL Anthology · ACL 2026

Transfer Learning for Generalizable Automated LLM Improvement Pipeline for IVR Navigation

Vishal Sankar Ram, Jason Kushner, Manas Paldhe, Youngseo Son

原文摘要

Administrative tasks in the healthcare domain share linguistic commonalities, but it can be time-consuming to manually design LLM prompts for each use case. When calling health insurers, interactive voice response (IVR) systems cause delays in patient care and increase provider burnout due to complex routing and long hold times. Thus, IVR navigation models can offer significant time savings and reduce barriers to care. We propose a production-quality automated LLM pipeline which leverages a small number of human-labeled ground truth datasets to transfer specialized prompts from one task to another; specifically, we perform a cross-task transfer of our IVR navigation logic, adapting the prompt from reaching the claims department to reaching the patient benefit department. Our approach reduces prompt complexity by up to 80% and obtains 82% turn-level accuracy in real-world industrial healthcare settings, surpassing a human-designed prompt at 79%.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.137ACL Anthology · ACL 2026

Beyond Instruction Optimization: Multi-Agent Error-Driven Class Description Refinement for LLM-Based Classification

Hamvir Dev, Shivam Ratnakant Mhaskar, Sasanka Vutla, Anup Pattnaik

原文摘要

Large Language Models (LLMs) have demonstrated considerable efficacy in classification tasks, yet their performance depends on two critical prompt components: Task Instructions (HOW to classify) and Class Descriptions (WHAT defines each class). While prompt engineering research has extensively explored instruction optimization, class descriptions have received comparatively less attention, often being treated as fixed inputs or simple label names. This represents a critical gap for real-world classification tasks, particularly in contact center domains, where labels often suffer from ambiguous boundaries, overlapping definitions, and incomplete coverage of possible cases—substantially limiting accuracy regardless of instruction quality.We propose a multi-agent framework for iteratively refining class descriptions based on classification errors. By analyzing misclassified instances, language agents automatically generate improved descriptions that better capture class distinctions and resolve ambiguities. Empirical evaluation across contact center and public benchmark datasets demonstrates upto 20.71% accuracy improvements over static class descriptions, addressing an orthogonal dimension to existing instruction optimization techniques.

cs.CL · cs.MA

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.138ACL Anthology · ACL 2026

TEN: Table Explicitization, Neurosymbolically

Nikita Mehrotra, Aayush Kumar, Sumit Gulwani, Arjun Radhakrishna, Ashish Tiwari

原文摘要

We present TEN, a neurosymbolic approach for extracting tabular data from semistructured text such as copy-pasted content from PDFs, emails, or OCR-flattened outputs. This task poses real-world challenges in domains like finance and healthcare, where manual copy-paste into spreadsheets introduces errors and OCR distortions compromise data integrity, leading to financial losses and flawed decisions.Purely neural methods suffer from hallucinations and structural inconsistencies, hindering deployment robustness. TEN addresses this via a novel triadic feedback loop that iteratively refines table hypotheses to enforce constraints and achieve verifiable convergence.Experiments show TEN outperforms neural baselines in exact match accuracy and lower hallucination rates. A 21-participant user study rates TEN tables more accurate and preferred in over 60% of pairwise comparisons, though verification and correction effort did not differ significantly between conditions.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.139ACL Anthology · ACL 2026

RADAR: Risk-Aware Distilled Adaptive Routing for Efficient Short-Form Video Platform Ecosystem Governance

Baoyu Jing, Zixuan Wang, Junwen Chen, Xin Dong, Bingfeng Deng

原文摘要

Large-scale integrity enforcement on short-form video platforms typically relies on multiple specialized vertical modules, each dedicated to a specific risk category. However, exhaustively executing these computationally intensive modules over massive content streams leads to substantial inference overhead, despite the fact that most content is benign and violations are usually confined to limited policy domains. To address this inefficiency, we propose RADAR, a lightweight risk-aware routing framework that selectively releases low-risk content while dispatching high-risk instances to appropriate vertical modules. Industrial deployment of such routing systems presents two major challenges: (1) systematic label sparsity caused by disjoint annotation pipelines across risk categories, and (2) the capacity-efficiency tradeoff inherent to compact routing architectures. To overcome these challenges, RADAR incorporates Validity-Aware Masking to handle fragmented supervision and Expert-Guided Knowledge Distillation to transfer knowledge from heavyweight expert models into the lightweight router. Experiments on large-scale real-world datasets demonstrate that the proposed masking strategy effectively mitigates disjoint annotation issues, while distillation substantially enhances routing accuracy, enabling the lightweight router to achieve competitive or superior performance compared to specialized expert models.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.14ACL Anthology · ACL 2026

Calibrating LLM Judges: Linear Probes for Fast and Reliable Uncertainty Estimation

Bhaktipriya Radharapu, Eshika Saxena, Kenneth Li, Chenxi Whitehouse, Adina Williams, Nicola Cancedda

原文摘要

As LLM-based judges become integral to industry applications, obtaining well-calibrated uncertainty estimates efficiently has become critical for production deployment. However, existing techniques, such as verbalized confidence and multi-generation methods, are often either poorly calibrated or computationally expensive. We introduce linear probes trained with a Brier score-based loss to provide calibrated uncertainty estimates from reasoning judges’ hidden states, requiring no additional model training. We evaluate our approach on both objective tasks (reasoning, mathematics, factuality, coding) and subjective human preference judgments. Our results demonstrate that probes achieve superior calibration compared to existing methods with x computational savings, generalize robustly to unseen evaluation domains, and deliver higher accuracy on high-confidence predictions. However, probes produce conservative estimates that underperform on easier datasets but may benefit safety-critical deployments prioritizing low false-positive rates. Overall, our work demonstrates that interpretability-based uncertainty estimation provides a practical and scalable plug-and-play solution for LLM judges in production.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.140ACL Anthology · ACL 2026

From Graph to Text and Back: Semantic Fidelity in Automated Industrial Knowledge Graphs

Kamyar Zeinalipour, Silvia Severini, Alessia Borghini, Sara Cardarelli, Marco Maggini, Marco Gori

原文摘要

Knowledge Graphs (KGs) are the backbone of reliable industrial data strategies, yet verbalizing them with Large Language Models (LLMs) often leads to unacceptable risks for high-stakes applications, such as hallucinations or omitted relations. To enforce strict semantic fidelity in KG-to-text generation, we introduce a self-supervised round-trip pipeline. The system verbalizes KG triples into text and immediately attempts to reconstruct the original graph from that text; only verbalizations that enable perfect graph recovery are retained. This creates a closed feedback loop that guarantees the generated text is semantically equivalent to the source data. Experiments confirm that our automated round-trip consistency score correlates strongly with expert judgment, effectively acting as a scalable proxy for human review. Furthermore, we show that standard LLMs can bootstrap their own KG-extraction and generation capabilities by fine-tuning on this trusted synthetic data. Our approach yields significant improvements in triple-extraction accuracy and verbalization faithfulness without relying on costly manual annotation or massive teacher models, offering a practical path to deploying trustworthy, KG-grounded AI systems.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.141ACL Anthology · ACL 2026

Learning Selective LLM Autonomy from Copilot Feedback in Enterprise Customer Support Workflows

Nikita Borovkov, Elisei Rykov, Olga Tsymboi, Sergei Filimonov, Nikita Surnachev, Dmitry Bitman, Anatolii Potapov

原文摘要

We present a deployed system that automates end-to-end customer support workflows inside an enterprise Business Process Management (BPM) platform. The approach is scalable in production and reaches selective automation within two weeks for a new process, leveraging supervision already generated at scale: structured per-case UI interaction traces and low-overhead copilot feedback, where operators either accept a suggestion or provide a correction. A staged deployment pipeline trains a next UI action policy, learns a critic from copilot feedback to calibrate abstention, and executes only high-confidence steps in the background while deferring uncertain decisions to operators and resuming from the updated UI state. This setup lets one operator supervise multiple concurrent sessions and be interrupted only when the system is uncertain. The system operates on a schema-driven view of the BPM interface and includes monitoring and safe fallbacks for production. In production, it automated 45% of sessions and reduced average handling time by 39% without degrading support quality level.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.142ACL Anthology · ACL 2026

Prompt-Level Distillation: A Non-Parametric Alternative to Model Fine-Tuning for Efficient Reasoning

Sanket Badhe, Deep Shah

原文摘要

Advanced reasoning typically requires Chain-of-Thought prompting, which is accurate but incurs prohibitive latency and substantial test-time inference costs. The standard alternative, fine-tuning smaller models, often sacrifices interpretability while introducing significant resource and operational overhead. To address these limitations, we introduce Prompt-Level Distillation (PLD). We extract explicit reasoning patterns from a Teacher model and organize them into a structured list of expressive instructions for the Student model’s System Prompt. Evaluated using Gemma-3 4B, PLD improved Macro F1 scores on StereoSet (57% to 90.0%) and Contract-NLI (67% to 83%), while increasing LogiQA accuracy to 70%. Similar results on Mistral Small 3.1 demonstrate cross-architecture generalizability, enabling these compact models to match frontier performance with negligible latency overhead. These expressive instructions render the decision-making process transparent, allowing for full human verification of logic, making this approach ideal for regulated industries such as law, finance, and content moderation, as well as high-volume use cases and edge devices.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.143ACL Anthology · ACL 2026

MTIVE: Multi-Task Image Verification Engine Using Vision-Language Models for E-commerce

Yu-Tong Cao, Vishnu Prabhakaran, Arunita Das, Purav Aggarwal, Anoop Saladi

原文摘要

Vision-language models show promise for e-commerce automation but struggle with noisy real-world images and multi-task requirements. We introduce MTIVE, a curriculum learning framework that progressively adapts base models through three stages: continued pre-training on large-scale e-commerce datasets with contrastive learning and diverse dialogue templates, instruction tuning on synthetic data, and modular task-specific expert training. Our architecture uses frozen base weights with stacked LoRA adapters—shared modules for domain knowledge and lightweight task-specific experts—enabling continual learning without catastrophic forgetting. MTIVE outperforms open-source and proprietary baselines in both standard and continual learning settings.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.144ACL Anthology · ACL 2026

Measuring and Mitigating Racial Bias in Embedding Models: A Comparative Study for Law Enforcement Retrieval

Archan Dutta

原文摘要

Embedding models are often used for semantic retrieval in high-stakes domains such as law enforcement, where biased outputs can have severe consequences. We systematically measure racial bias in six widely used embedding models by computing similarity scores between crime incident texts that include racial identity tokens and simple law enforcement queries. The analysis reveals that racial descriptors consistently affect cosine similarity scores and retrieval rankings for semantically identical crime incidents. All models exhibit statistically significant bias, with magnitude varying across models. This study provides a comprehensive methodology and metrics to aid the selection of embedding models when deploying NLP-based systems in the law enforcement domain. Organizations can reduce bias at low cost through informed model selection. The methodology establishes reproducible metrics for measuring bias in embedding-based systems.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.145ACL Anthology · ACL 2026

VishBox v2: A Multi-Agent System for Adaptive Voice Phishing Simulation

Sungmi Park, Daon Choi, Yoonmo Yang, Hong Yunyi, Heedou Kim

原文摘要

Voice phishing is a multi-round social engineering attack in which strategy and victim psychology co-evolve, yet real transcripts are rarely accessible for systematic analysis. We present VishBox v2, a multi-agent architecture that generates structured phishing simulations grounded in crime-script procedures and persuasion principles. A Main Agent orchestrates a Dialogue Agent and a Tactic Search Agent, combining multi-round dialogue generation, web-based tactic mining, and emotion-driven vulnerability tracking. Across 571 rounds, results including police-expert evaluation support procedural realism and show that VishBox v2 captures tactic concentration, vulnerability transitions, and web-search-induced procedural disruptions. The framework provides a controlled foundation for safer red-teaming and security training research.

cs.CL · cs.MA

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.146ACL Anthology · ACL 2026

ResoDiff-44k: High-Fidelity Cross-Lingual Speech and Singing Synthesis via Discrete Diffusion

Gyanendra Das, Sai Satyam Jena

原文摘要

While large-scale generative speech models have achieved remarkable semantic coherence, industrial deployment remains constrained by a fidelity ceiling typically capped at lower sampling rates. A fundamental limitation is the reliance on intermediate mel-spectrograms, a low-dimensional bottleneck that discards phase and high-frequency information, causing artifacts in expressive scenarios like singing. In this work, we introduce ResoDiff-44k, a production-grade generative foundation model designed for cinema-quality, 44.1kHz audio synthesis. Departing from standard masked audio modeling and mel-spectrogram inversion, ResoDiff-44k leverages Discrete Diffusion over a pure Descript Audio Codec latent space. We pre-train ResoDiff-44k on a massive 150K -hour multilingual dataset to establish a robust acoustic prior, followed by targeted fine-tuning on a curated regional mixed-language and singing corpus. Our experiments demonstrate that replacing the standard prediction head with a discrete diffusion trajectory significantly reduces misalignment in long sequences. We report a double-blind subjective evaluation showing that ResoDiff-44k achieves a 4.6 Mean Opinion Score in 44.1kHz singing synthesis and a 71% reduction in character error rate on regional mixed-language prompts compared to strong baselines. The proposed pipeline offers a viable path for deploying high-fidelity, culturally adaptive conversational agents.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.147ACL Anthology · ACL 2026

TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale

Jun Wang, Ziyin Zhang, Rui Wang, Hang Yu, Peng Di, Rui Wang

原文摘要

Real-time detection and mitigation of technical anomalies are critical for large-scale cloud-native services, where even minutes of downtime can result in massive financial losses and diminished user trust. While customer incidents serve as a vital signal for discovering risks missed by monitoring, extracting actionable intelligence from this data remains challenging due to extreme noise, high throughput, and semantic complexity of diverse business lines. In this paper, we present TingIS, an end-to-end system designed for enterprise-grade incident discovery. At the core of TingIS is a multi-stage event unification engine that synergizes efficient indexing techniques with Large Language Models (LLMs) to make informed decisions on event merging, enabling the stable extraction of actionable incidents from just a handful of diverse user descriptions. This engine is complemented by a cascaded routing mechanism for precise business attribution and a multi-dimensional noise reduction pipeline that integrates domain knowledge, statistical patterns, and behavioral filtering. Deployed in a production environment handling a peak throughput of over 2,000 messages per minute and 300,000 messages per day, TingIS achieves a P90 alert latency of 3.5 minutes and a 95% discovery rate for high-priority incidents. Benchmarks constructed from real-world data demonstrate that TingIS significantly outperforms baseline methods in routing accuracy, clustering quality, and Signal-to-Noise Ratio.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.148ACL Anthology · ACL 2026

LoCar: Localization-Aware Evaluation of In-Vehicle Assistants through Fine-Grained Sociolinguistic Control

Seogyeong Jeong, Kiwoong Park, Seyoung Song, Eunsu Kim, Ken E Friedl, Jaeho Kim, Alice Oh

原文摘要

While Large Language Models (LLMs) are increasingly integrated into in-vehicle conversational systems, identifying the optimal model remains challenging due to the lack of domain-specific evaluation standards tailored to real-world deployment requirements. In this paper, we propose a novel evaluation framework for in-vehicle assistants, with a particular focus on Korean-language localization. Our empirical analysis reveals notable patterns in model behavior. First, fine-grained Korean honorific control remains unstable in current LLMs, indicating that precise speech-level realization must be explicitly evaluated in localization settings. Second, models exhibit weaker performance in strategic conversational metrics like clarification and proactivity. Our analysis suggests this stems from the inherent subjective complexity of these tasks, where our framework adopts a conservative evaluation stance to prioritize reliability. Together, our findings underscore that automotive AI must move beyond general competence toward precise linguistic tailoring and reliable, safety-oriented interaction management.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.149ACL Anthology · ACL 2026

Index-Time Prefix Injection for Multi-Tenant Retrieval: Improving Search Relevance Without Model Fine-Tuning

Vaibhav Varshney, Manjunatha Naik Mc

原文摘要

Multi-tenant enterprise search platforms serve hundreds of customers through a single shared retrieval model. Fine-tuning on individual customer data is typically prohibited by contractual and regulatory constraints, and maintaining per-customer models does not scale. We present index-time prefix injection, a training-free method that improves retrieval relevance by prepending domain-descriptive natural-language prefixes to documents during indexing. For example, prepending “IT service management knowledge article:” to an IT knowledge base shifts its embeddings into a tighter, more domain-coherent region of the vector space. Prefixes are discovered through a tiered strategy: LLM-based generation from document samples when data policies allow, domain-expert curation when they do not, and a standardized prefix library as fallback. Deployed across 18 languages and 400+ customer instances, the approach yields 3–8% Hit@5 improvements with zero model training. A/B tests confirm a 4.2% CTR lift. We describe the system design, evaluation at scale, and deployment lessons including failure modes.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

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

Detecting AI-Generated Content on Social Media with Multi-modal Language Models

Chenyang Yang, Shen Yan, Yibo Yang, Litao Hu, Yuchen Liu, Yuan Zeng, Hanchao Yu, Yinan Zhu 等 16 位作者

原文摘要

Generative AI has enabled the creation of photorealistic images and videos that are increasingly disseminated on social media, often used for spam, misinformation, manipulation, and fraud. Existing AI-generated content (AIGC) detection methods face challenges including poor generalization to new generation models, reliance on single modalities, and lack of interpretable explanations. We present our pipeline that mitigates these issues by continuously curating diverse multi-modal social media data and training a compact vision-language model for detection and explanation. Our model achieves state-of-the-art detection performance on public benchmarks and demonstrates robust detection and explanation capabilities on internal social media datasets across multiple platforms. We deployed our model for post recommendation on social media platforms and observed positive downstream impacts on user engagement, demonstrating that it is feasible to perform effective AIGC detection in dynamic, real-world social media environments.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.150ACL Anthology · ACL 2026

Benchmarking Testing in Automated Theorem Proving

Jongyoon Kim, Hojae Han, Seung-Won Hwang

原文摘要

Recent advances in large language models (LLMs) have shown promise in formal theorem proving, yet evaluating semantic correctness remains challenging. Existing evaluations rely on indirect proxies such as lexical overlap with human-annotated proof,or expensive manual inspection.Inspired by the shift from lexical comparison to test-based evaluation in code generation, we propose T^2, a framework that evaluates the semantic correctness of formal theorems: a generated theorem is considered correct only if all dependent successor theorems compile successfully, analogous to integration testing.We construct a benchmark from 5 real-world Lean 4 repositories, comprising 2,206 problems paired with 41 successor theorems on average, automatically extracted without human effort.Experiments demonstrate that while state-of-the-art models achieve high compilation success, they perform significantly worse under our semantic metric.The best model, Claude-Sonnet-4.5, achieves only 38.9% Testing Accuracy on the full set, given both natural language proof and successor theorems as context, revealing a critical gap in current theorem generation capabilities.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.151ACL Anthology · ACL 2026

Graph-Based Phonetic Error Correction of Noisy ASR

Pratik Rakesh Singh, Mohammadi Zaki, Aneesh Mukkamla, Pankaj Wasnik

原文摘要

Automatic speech recognition (ASR) systems, despite low overall word error rates, produce residual lexical errors that disproportionately affect semantically critical tokens such as named entities, negations, and sentiment-bearing words. These errors are often structured, arising from phonetic similarity rather than random noise, making naive token-level correction insufficient.We propose a structured ASR correction framework, that we call G-SPIN, that combines phonetic graph modeling with contextual language understanding. A graph neural network (GNN) first constructs acoustically plausible candidate neighborhoods for flagged tokens, explicitly restricting the correction search space to phonetic alternatives. A masked language model (MLM) then provides local contextual scoring, and an instruction-tuned large language model (LLM) performs final context-aware re-ranking over this compact candidate set. By decoupling structured phonetic reasoning from contextual semantic selection, our method avoids unconstrained generation while improving correction accuracy. The framework is lightweight, modular, and operates entirely at inference time.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.152ACL Anthology · ACL 2026

CAT: Confidence-Adaptive Thinking for Efficient Reasoning of Large Reasoning Models

Qizhi Jiang, Shuo Wang, Pei Ke, Yuhang Song, Ke Qin

原文摘要

Large Reasoning Models (LRMs) have achieved remarkable success on complex tasks by leveraging long chain-of-thought (CoT) trajectories, yet they frequently exhibit overthinking on simple queries, resulting in significant token overhead and reduced inference efficiency. However, existing compression methods predominantly apply uniform length reduction or rely on coarse-grained difficulty estimation, often leading to performance degradation on difficult problems. To address this limitation, we propose Confidence-Adaptive Thinking (CAT), a framework that incorporates the model’s intrinsic self-certainty signals as confidence into the preference optimization process, which autonomously modulates reasoning lengths based on problem difficulty. Experimental results show that CAT consistently outperforms state-of-the-art baselines on reasoning accuracy across multiple benchmarks on different base models. Our work enables LRMs to effectively compress confident responses while deliberating on uncertain ones, offering a potentially robust solution for balancing accuracy and latency in practical industrial scenarios.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.153ACL Anthology · ACL 2026

LatentGate: Low-Latency Semantic Routing via Frozen-Backbone Probing of Small Language Models

Shivam Ratnakar, Abhiroop Talasila, Vinayak K Doifode

原文摘要

As Multi-Agent Systems scale to hundreds of specialized agents, routing becomes a critical bottleneck. Prompt-based LLM routers deliver strong semantic reasoning but incur prohibitive latency (~1500–2000ms) and cost that scales with agent count, while embedding-based routers are fast (25–50ms) but collapse semantically similar yet functionally distinct agents. We identify representation anisotropy, the geometric collapse of hidden-state vectors into a narrow cone, as a key mechanism underlying embedding-based routing failure. We propose LatentGate, a non-generative router that extracts mean-pooled hidden states from a frozen small language model (SLM), applies PCA-whitening to resolve the anisotropy, and trains a lightweight linear probe for agent classification. Across 5 SLM backbones and 100 enterprise agents, LatentGate achieves 98.8% in-domain and 80.0% OOD accuracy on natural queries, 13–22 absolute points above embedding baselines, and 92.9% on CLINC150. It takes ~28ms to run on a T4 GPU, with the SLM forward pass independent of agent count and classification adding a negligible O(Ck) term. We demonstrate the potential of using a lightweight linear probe to enable sub-10ms warm-start retraining from user feedback, providing a foundation for continual learning in production environments. Benchmarking prompt-based routing with GPT-4.1, GPT-4.1-nano, and Gemini 2.5 Flash confirms degradation to 70–77% accuracy at 100 agents with 1500–2000ms latency, motivating non-generative alternatives.

cs.CL · cs.MA

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.154ACL Anthology · ACL 2026

Thinking with Reasoning Skills: Fewer Tokens, More Accuracy

Guangxiang Zhao, Qilong Shi, Xusen Xiao, Xiangzheng Zhang, Tong Yang, Lin Sun

原文摘要

Reasoning LLMs often spend substantial tokens on long intermediate reasoning traces (e.g., chain-of-thought) when solving new problems. We propose to summarize and store reusable reasoning skills distilled from extensive deliberation and trial-and-error exploration, and to retrieve these skills at inference time to guide future reasoning. Unlike the prevailing reasoning from scratch paradigm, our approach first recalls relevant skills for each query, helping the model avoid redundant detours and focus on effective solution paths. We evaluate our method on coding and mathematical reasoning tasks, and find that it significantly reduces reasoning tokens while improving overall performance. The resulting lower per-request cost indicates strong practical and economic potential for real-world deployment.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.16ACL Anthology · ACL 2026

IceBreaker for Conversational Agents: Breaking the First-Message Barrier with Personalized Starters

Hongwei Zheng, Weiqi Wu, Zhengjia Wang, Guanyu Jiang, Haoming Li, Tianyu Wu, Yongchun Zhu, Jingwu Chen 等 9 位作者

原文摘要

Conversational agents, such as ChatGPT and Doubao, have become essential daily assistants for billions of users. To further enhance engagement, these systems are evolving from passive responders to proactive companions. However, existing efforts focus on activation within ongoing dialogues, while overlooking a key real-world bottleneck. In the conversation initiation stage, users may have a vague need but no explicit query intent, creating a first-message barrier where the conversation holds before it begins. To overcome this, we introduce Conversation Starter Generation: generating personalized starters to guide users into conversation. However, unlike in-conversation stages where immediate context guides the response, initiation must operate in a cold-start moment without explicit user intent. To pioneer in this direction, we present IceBreaker that frames human ice-breaking as a two-step handshake: (i) evoke resonance via Resonance-Aware Interest Distillation from session summaries to capture trigger interests, and (ii) stimulate interaction via Interaction-Oriented Starter Generation, optimized with personalized preference alignment and a self-reinforced loop to maximize engagement. Online A/B tests on one of the world’s largest conversational agent products show that IceBreaker improves user active days by +1.84‰ and click-through rate by +94.25‰, and has been deployed in production.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

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

Motivating Next-Gen Accelerators with Flexible N{:}M Activation Sparsity via Benchmarking Lightweight Post-Training Sparsification Approaches

Shirin Alanova, Kristina Kazistova, Ekaterina Galaeva, Alina Kostromina, Vladimir Smirnov, Redko Dmitry, Alexey Dontsov, Maxim Zhelnin 等 10 位作者

原文摘要

The demand for efficient large language model inference has spurred interest in sparsification, yet current hardware support remains narrowly focused on 2:4 weight sparsity. In this work, we argue that activation sparsity despite being overlooked in hardware design offers a promising path for dynamic, input-adaptive compression with significant I/O and memory benefits. We present a comprehensive post-training study of N{:}M activation pruning across four LLMs (Llama2-7B-chat, Llama3.1-8B-Instruct, Qwen2.5-7B-Instruct, Gemma3-4B-Instruct), demonstrating that activation pruning consistently outperforms weight pruning at matched sparsity levels. We evaluate lightweight, plug-and-play error mitigation and selection strategies that require minimal or no calibration data across four sparsity patterns: 2:4, 4:8, 8:16, and 16:32. Among these, 16:32 approaches the performance of unstructured 50% sparsity and is is approximately 2.7\times better than 2:4, while 8:16 offers an optimal balance of accuracy and practicality. Our results provide evidence that next-generation accelerators should consider native support for N{:}M activation sparsity and can serve as a strong baseline for the future methods.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

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

Sommelier: Scalable Open Multi-turn Audio Pre-processing for Full-duplex Speech Language Models

Kyudan Jung, Jihwan Kim, Soyoon Kim, Jeonghoon Kim, Jaegul Choo, Cheonbok Park

原文摘要

As the paradigm of AI shifts from text-based LLMs to Speech Language Models (SLMs), there is a growing demand for full-duplex systems capable of real-time, natural human-computer interaction.However, the development of such models is constrained by the scarcity of high-quality, multi-speaker conversational data, as existing large-scale resources are predominantly single-speaker or limited in volume.Addressing the complex dynamics of natural dialogue, such as overlapping and back-channeling remains a challenge, with standard processing pipelines suffering from diarization errors and ASR hallucinations.To bridge this gap, we present a robust and scalable open-source data processing pipeline designed for full-duplex model.Our code and project page are publicly available at https://anonymous-2001-j.github.io/sommelier.github.io/.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

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

Proximity-Based Multi-Turn Optimization: Practical Credit Assignment for LLM Agent Training

Yangyi Fang, Jiaye Lin, Xiaoliang Fu, Cong Qin, Haolin Shi, Chang Liu

原文摘要

Multi-turn LLM agents are becoming pivotal to production systems, spanning customer service automation, e-commerce assistance, and interactive task management, where accurately distinguishing high-value informative signals from stochastic noise is critical for sample-efficient training. In real-world scenarios, a failure in a trivial task may reflect random instability, whereas success in a high-difficulty task signifies a genuine capability breakthrough. Yet, existing group-based policy optimization methods rigidly rely on statistical deviation within discrete batches, frequently misallocating credit when task difficulty fluctuates. To address this issue, we propose Proximity-based Multi-turn Optimization (ProxMO), a practical and robust framework engineered specifically for the constraints of real-world deployment. ProxMO integrates global context via two lightweight mechanisms: success-rate-aware modulation dynamically adapts gradient intensity based on episode-level difficulty, while proximity-based soft aggregation derives baselines through continuous semantic weighting at the step level. Extensive evaluations on ALFWorld and WebShop benchmarks demonstrate that ProxMO yields substantial performance gains over existing baselines with negligible computational cost. Ablation studies further validate the independent and synergistic efficacy of both mechanisms. Crucially, ProxMO offers plug-and-play compatibility with standard GRPO frameworks, facilitating immediate, low-friction adoption in existing industrial training pipelines. Our implementation is available at: https://github.com/GithubX-F/ProxMO-RL.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

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

Smarter, not Bigger: Fine-Tuned RAG-Enhanced LLMs for Automotive Hardware-in-the-Loop Testing

Chao Feng, Zihan Liu, Siddhant Gupta, Jan von der Assen

原文摘要

Hardware-in-the-Loop (HIL) testing is essential for automotive validation but suffers from fragmented and underutilized test artifacts. This paper presents HIL-GPT, an industry-deployed retrieval-augmented generation (RAG) system that integrates semantic retrieval with domain-adapted large language models to support test engineers in real-world HIL workflows. The system combines domain-specific embeddings to enable traceable retrieval of test cases and requirements under industrial latency and cost constraints. Through empirical evaluation, we show that compact, domain-adapted models can achieve a favorable trade-off among accuracy, latency, and cost compared to larger general-purpose models, challenging the assumption that larger models are always preferable in industrial NLP systems. An A/B user study further confirms that HIL-GPT improves perceived helpfulness, truthfulness, and satisfaction over general-purpose LLMs.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表