Zhanzhan Zhao, Yi Yang, Xiao Jia, Zeyun Dong, Chenzhang Wang
As large language models (LLMs) blur the boundary between cognition and computation, they reshape knowledge production and collective life. We combine a curated review of 198 papers with computational mapping of 47,719 formally published papers from five databases. Text embeddings, clustering, and topic modeling organize the curated literature into 13 subcategories across three domains. LLM as Social Minds examines mind-like reasoning and socially grounded behavior in individual models. LLM Societies explores how interactions among agents produce collective behavior and social organization. LLM-Human Interactions examines how people perceive and engage with LLMs, and how interaction reshapes human thinking, creativity, and relationships. Structural topic modeling of the larger corpus traces how research attention shifts over time and differs across publication venues. We present a reproducible framework that identifies the field's underlying structure and enables its continuous refinement as new studies emerge. The review also identifies a gap in current understanding: the mechanisms underlying many of LLMs' observed cognitive and social behaviors remain unclear. Human-like performance can then be mistaken for understanding, obscuring the norms, biases, and structures of power that LLMs reproduce. We call for shared benchmarks and robustness checks across the three domains to build a reliable evidence base for understanding LLMs' social behaviors and informing their design and use.
Recent research on Multimodal Sentiment Analysis (MSA) has focused on learning from language, visual, and acoustic modalities with incomplete data to infer human sentiment. Most studies typically compensate for missing information by reconstructing modality features or designing complicated fusion mechanisms. However, these methods still suffer from spurious generation and noisy guidance due to the lack of high-level semantic grounding in partially observed multimodal evidence. To address these issues, we propose SemMSA, a latent semantic-aided framework that constructs rich sentiment-relevant semantics with LLMs, fully integrating with all modalities via anchor-free spectral alignment. It mainly consists of Cross-modal Semantic Refinement (CSR) and Cross-modal Spectral Alignment (CSA). Specifically, CSR first adaptively extracts visual and acoustic representations by corresponding adapters to form a unified multimodal prefix with language in the frozen LLM embedding space. It then iteratively produces continuous discriminative semantic states through a token-efficient latent refinement process without decoding explicit text. Next, CSA simultaneously aligns the refined semantics with all modalities by enhancing the dominant spectral component of their kernel Gram matrix. This captures global nonlinear dependencies among all representations without relying on a predefined anchor modality. In addition, an instance-level spectral separation constraint preserves cross-sample discriminability and mitigates representation collapse. Extensive experiments on SIMS, MOSI, and MOSEI benchmarks demonstrate that SemMSA achieves state-of-the-art performance.
Wei Shao Institute of Computing Technology, Chinese Academy of Sciences, Yihang Wang ByteDance Inc. and University of Chinese Academy of Sciences, Gao yu Zhu, Ziqiang Cheng, Lei Yu, Jiafeng Guo Institute of Computing Technolgy, Chinese Academy of Sciences, Xueqi Cheng Institute of Computing Technology, Chinese Academy
Existing detoxification methods for large language models mainly focus on post-training stage or inference time, while few tackle the source of toxicity, namely, the dataset itself. Such training-based or controllable decoding approaches cannot completely suppress the model’s inherent toxicity, whereas detoxifying the pretraining dataset can fundamentally reduce the toxicity that the model learns during training. Hence, we attempt to detoxify directly on raw corpora with SoCD (Soft Contrastive Decoding), which guides an LLM to localize and rewrite toxic spans in raw data while preserving semantics, in our proposed HSPD (Hierarchical Semantic-Preserving Detoxification) pipeline, yielding a detoxified corpus that can drop-in replace the original for fine-tuning or other training. On GPT2-XL, HSPD attains state-of-the-art detoxification, reducing Toxicity Probability (TP) from 0.42 to 0.18 and Expected Maximum Toxicity (EMT) from 0.43 to 0.20. We further validate consistent best-in-class results on LLaMA2-7B, OPT-6.7B, and Falcon-7B. These findings show that semantics-preserving, corpus-level rewriting with HSPD effectively suppresses downstream toxicity while retaining data utility and allowing seamless source-level mitigation, thereby reducing the cost of later model behavior adjustment.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Huilin Deng, Hongchen Luo Northeastern University, Yue Zhu, Long Li, Zhuoyue Chen Alibaba, Xinghao Zhao, Ming LI Hithink RoyalFlush Information Network Co.,Ltd., Chuyang Zhao Baidu 等 12 位作者
Despite recent advances in Reinforcement learning with verifiable rewards (RLVR) for large language model (LLM) reasoning, most methods suffer from exploration collapse, as the semantic homogeneity of random rollouts traps models in narrow, over-optimized behaviors. Existing methods leverage policy entropy to encourage exploration, but face inherent limitations: global entropy regularization is susceptible to reward hacking, inducing meaningless verbosity, whereas local token-selective updates struggle with the strong inductive bias of pre-trained models. To this end, we propose Latent Policy Optimization via Iterative Information Bottleneck ( I²B-LPO), which shifts from statistical perturbation of token distributions to topological branching of reasoning trajectories. I²BLPO triggers latent branching at high-entropy states to diversify reasoning trajectories and applies the Information Bottleneck as a trajectory filter and self-reward to ensure concise and informative exploration. Empirical results on four mathematical benchmarks demonstrate that I²B-LPO achieves state-of-the-art performance, with margins of up to 5.3% in accuracy and 7.4% in diversity metrics. Code is available at https://github.com/denghuilin-cyber/IIB-LPO.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Yihang Wang ByteDance Inc. and University of Chinese Academy of Sciences, Bin Wu, Yueyang Su Chinese Academy of Sciences, Tianfu Zhang ByteDance Inc., Yiqi Du ByteDance Inc., Lei Yu, Jiafeng Guo Institute of Computing Technolgy, Chinese Academy of Sciences, Xueqi Cheng Institute of Computing Technology, Chinese Academy
Large embedding models have become the backbone of modern retrieval systems, offering strong semantic representations at the cost of substantial storage and computation. While recent work explores quantizing embeddings into discrete document identifiers for generative retrieval, most existing approaches rely on Euclidean quantization, which is poorly aligned with the angular geometry induced by contrastive embedding training and often requires long identifier sequences to preserve semantic fidelity. In this work, we propose Hyperspherical Householder Quantization (HHQ), a geometry-aware distillation method that compresses large embeddings into short discrete representations via iterative Householder transformations on the unit hypersphere. By explicitly preserving cosine similarity at each step, HHQ distills semantic structure into compact identifiers that remain faithful to the original embedding space. To support reliable generation of these identifiers, we introduce constrained supervised fine-tuning and tree-aware dynamic masking to enforce structural validity during training and inference. Experiments on NQ and MS MARCO show that HHQ achieves competitive or superior retrieval performance using only five tokens per document, substantially reducing decoding cost while retaining strong semantic retrieval accuracy.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Xianren Zhang, Shreyas Prasad Amazon, Di Wang Amazon, Qiuhai Zeng Amazon, Suhang Wang Pennsylvania State University, Wenbo Yan Amazon, Mat Hans Amazon
Web agents have shown great promise in performing many tasks on e-commerce websites. To assess their capabilities, several benchmarks have been introduced. However, current benchmarks in the e-commerce domain face two major problems. First, they primarily focus on product search tasks (e.g., “Find an Apple Watch”), failing to capture the broader range of functionalities offered by real-world e-commerce services such as Amazon, including account management and gift card operations. Second, existing benchmarks typically evaluate whether the agent completes the user query, but ignore the potential risks involved. In practice, web agents can make unintended changes that negatively impact the user’s account or status. For instance, an agent might purchase the wrong item, delete a saved address, or incorrectly configure an auto-reload setting. To address these gaps, we propose a new benchmark called Amazon-Bench. To generate user queries that cover a broad range of tasks, we propose a data generation pipeline that leverages webpage content and interactive elements (e.g., buttons, check boxes) to create diverse, functionality-grounded user queries covering tasks such as address management, wishlist management, and brand store following. To enhance agent evaluation, we propose an automated evaluation framework that assesses both the performance and safety of web agents. We systematically evaluate various agents, finding that current agents struggle with complex queries and pose safety risks. These results highlight the need for developing more robust and reliable web agents.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Shuhua Yang, Jiahao Zhang, Yilong Wang, Dongwon Lee The Pennsylvania State University, Suhang Wang Pennsylvania State University
Graph-based retrieval-augmented generation (GraphRAG) systems construct knowledge graphs over document collections to support multi-hop reasoning. While prior work shows that GraphRAG responses may leak retrieved subgraphs, the feasibility of query-efficient reconstruction of the hidden graph structure remains unexplored under realistic query budgets. We study a budget-constrained black-box setting where an adversary adaptively queries the system to steal its latent entity–relation graph. We propose AGEA (Agentic Graph Extraction Attack), a framework that leverages a novelty-guided exploration–exploitation strategy, external graph memory modules, and a two-stage graph extraction pipeline combining lightweight discovery with LLM-based filtering. We evaluate AGEA on medical, agriculture, and literary datasets across Microsoft-GraphRAG and LightRAG systems. Under identical query budgets, AGEA significantly outperforms prior attack baselines, recovering up to 90% of entities and relationships while maintaining high precision. These results demonstrate that modern GraphRAG systems are highly vulnerable to structured, agentic extraction attacks, even under strict query limits. The code is available at https://github.com/shuashua0608/AGEA.
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cs.CL · cs.AI
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
JiYan Liu Qingdao University of Science and Technology, Youzheng Liu, Taihang Wang, Yimin Wang Qingdao University of Science and Technology, Ye Jiang Qingdao University of Science and Technology, Diana Maynard University of Sheffield
Protecting public figures from online abuse requires models that go beyond post-level classification to determine whether abuse is directed at a designated target, characterize the abuse intent, and extract textual evidence. We introduce a Target-Aware Multilingual Abuse (TAMA), benchmark of 9,386 X (Twitter) posts aimed at public figures, with aligned supervision for (i) tri-class target detection, (ii) 12-way fine-grained abuse type classification, and (iii) phrase-level abusive spans localization. To exploit the hierarchical coupling of these tasks, we propose Cascaded-MTL, a dependency-aware multi-task framework that conditions downstream predictions on upstream beliefs via three lightweight modules: Cross-Task Feature Fusion (CTF), Task-Adaptive Gating (TAG), and Label-Guided Span Detection (LGSD). Experiments across three multilingual encoders show that Cascaded-MTL consistently yields higher average F1 than single-task and standard multi-task training and delivers robust gains on type classification and span localization. The code and the dataset are released here: https://github.com/zgjiangtoby/CASCADED-MTL
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表