China

Consensus formation is pivotal in multi-agent systems (MAS), balancing collective coherence with individual diversity. Conventional LLM-based MAS primarily rely on explicit coordination, e.g., prompts or voting, risking premature homogenization. We argue that implicit consensus, where agents exchange information yet independently form decisions via in-context learning, can be more effective in dynamic environments that require long-horizon adaptability. By retaining partial diversity, systems can better explore novel strategies and cope with external shocks. We formalize a consensus-diversity tradeoff, showing conditions where implicit methods outperform explicit ones. Experiments on three scenarios -- Dynamic Disaster Response, Information Spread and Manipulation, and Dynamic Public-Goods Provision -- confirm partial deviation from group norms boosts exploration, robustness, and performance. We highlight emergent coordination via in-context learning, underscoring the value of preserving diversity for resilient decision-making.

EMNLP 2025

The Hidden Strength of Disagreement: Unraveling the Consensus-Diversity Tradeoff in Adaptive Multi-Agent Systems

in-context adaptation

implicit consensus

nlp tools for social analysis

multi-agent systems

human behavior analysis

The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. 

AAAI 2021

poster

## Welcome!
"I am excited to welcome you to this year’s edition of the Conference on Empirical Methods in Natural Language Processing! Importantly, it marks the 30th edition of EMNLP. With over 8,000 submissions, more than 3,000 accepted papers, and thousands of attendees, we have come a long way from that first
workshop, which had 14 accepted papers. As the field looks ahead, Suzhou is the fitting location for celebrating this milestone: rooted in a long literary tradition, yet modern and forward-looking, and home to a large share of the NLP community."<br>

*Message from the General Chair, Dirk Hovy*





<br>

Celebrate 30 Years of EMNLP! 
EMNLP 2025 will be held in Suzhou, China from November 5th to November 9th, 2025.

Large language models (LLMs) often exhibit societal biases in their outputs, prompting ethical concerns regarding fairness and harm. In this work, we propose KLAAD (KL-Attention Alignment Debiasing), an attention-based debiasing framework that implicitly aligns attention distributions between stereotypical and anti-stereotypical sentence pairs without directly modifying model weights. KLAAD introduces a composite training objective combining Cross-Entropy, KL divergence, and Triplet losses, guiding the model to consistently attend across biased and unbiased contexts while preserving fluency and coherence. Experimental evaluation of KLAAD demonstrates improved bias mitigation on both the BBQ and BOLD benchmarks, with minimal impact on language modeling quality. The results indicate that attention-level alignment offers a principled solution for mitigating bias in generative language models.

KLAAD: Refining Attention Mechanisms to Reduce Societal Bias in Generative Language Models

Understanding the overall stance of news articles is challenging due to their length and structural complexity. Yet, it is essential for supporting pluralistic and credible media environments. This paper introduces a novel stance detection dataset for Korean news, featuring annotations at both the article level and the segment level, informed by the narrative structure of news articles. Building on this resource, we propose an agentic in-context learning method that prompts a large language model (LLM) with segment-level stance predictions generated by a language model agent. Experiments across multiple LLMs demonstrate the effectiveness of the proposed framework for article-level stance detection and highlight its broader utility in enhancing diverse news recommendations and analyzing patterns of media bias.

Journalism-Guided Agentic In-context Learning for News Stance Detection

To confront the ever-evolving safety risks of LLMs, automated jailbreak attacks have proven effective for proactively identifying security vulnerabilities at scale. Existing approaches, including GCG and AutoDAN, modify adversarial prompts to induce LLMs to generate responses that strictly follow a fixed affirmative template. However, we observed that the reliance on the rigid output template is ineffective for certain malicious requests, leading to suboptimal jailbreak performance. In this work, we aim to develop a method that is universally effective across all hostile requests. To achieve this, we explore LLMs' intrinsic safety mechanism: a refusal stance towards the adversarial prompt is formed in a confined region and ultimately leads to a rejective response. In light of this, we propose Stance Manipulation (SM), a novel automated jailbreak approach that generates jailbreak prompts to suppress the refusal stance and induce affirmative responses. Our experiments across four mainstream open-source LLMs demonstrate the superiority of SM's performance. Under commenly used setting, SM achieves success rates over 77.1% across all models on Advbench. Specifically, for Llama-2-7b-chat, SM outperforms the best baseline by 25.4%. In further experiments with extended iterations in a speedup setup, SM achieves over 92.2% attack success rate across all models. Our code is publicly available at https://anonymous.4open.science/r/Stance-Manipulation-D5F0

Jailbreak LLMs through Internal Stance Manipulation

Ethical decision-making is a critical aspect of human judgment, and the growing use of LLMs in decision-support systems necessitates a rigorous evaluation of their moral reasoning capabilities. However, existing assessments primarily rely on single-step evaluations, failing to capture how models adapt to evolving ethical challenges. Addressing this gap, we introduce the Multi-step Moral Dilemmas (MMDs), the first dataset specifically constructed to evaluate the evolving moral judgments of LLMs across 3,302 five-stage dilemmas. This framework enables a fine-grained, dynamic analysis of how LLMs adjust their moral reasoning across escalating dilemmas. Our evaluation of nine widely used LLMs reveals that their value preferences shift significantly as dilemmas progress, indicating that models recalibrate moral judgments based on scenario complexity. Furthermore, pairwise value comparisons demonstrate that while LLMs often prioritize the value of care, this value can sometimes be superseded by fairness in certain contexts, highlighting the dynamic and context-dependent nature of LLM ethical reasoning. Our findings call for a shift toward dynamic, context-aware evaluation paradigms, paving the way for more human-aligned and value-sensitive development of LLMs.

The Staircase of Ethics: Probing LLM Value Priorities through Multi-Step Induction to Complex Moral Dilemmas

State-of-the-art (SOTA) Automatic Speech Recognition (ASR) systems primarily rely on acoustic information while disregarding additional multi-modal context. However, visual information are essential in disambiguation and adaptation. While most work focus on speaker images to handle noise conditions, this work also focuses on integrating presentation slides for the use cases of scientific presentation. In a first step, we create a benchmark for multi-modal presentation including an automatic analysis of transcribing domain-specific terminology. Next, we explore methods for augmenting speech models with multi-modal information. We mitigate the lack of datasets with accompanying slides by a suitable approach of data augmentation. Finally, we train a model using the augmented dataset, resulting in a relative reduction in word error rate of approximately 34%, across all words and 35%, for domain-specific terms compared to the baseline model.

Do Slides Help? Multi-modal Context for Automatic Transcription of Conference Talks

As large language models (LLMs) are increasingly used in morally sensitive domains, it is crucial to understand how persona traits affect their moral reasoning and persuasive behavior. We present the first large-scale study of multi-dimensional persona effects in AI-AI debates over real-world moral dilemmas. Using a 6-dimensional persona space (age, gender, country, class, ideology, and personality), we simulate structured debates between AI agents over 131 relationship-based cases. Our results show that personas affect initial moral stances and debate outcomes, with political ideology and personality traits exerting the strongest influence. Persuasive success varies across traits, with liberal and open personalities reaching higher consensus and win rates. While logit-based confidence grows during debates, emotional and credibility-based appeals diminish, indicating more tempered argumentation over time. These trends mirror findings from psychology and cultural studies, reinforcing the need for persona-aware evaluation frameworks for AI moral reasoning.

Synthetic Socratic Debates: Examining Persona Effects on Moral Decision and Persuasion Dynamics

We present the first large-scale computational study of political delegitimization discourse (PDD), defined as symbolic attacks on the normative validity of political entities. We curate and manually annotate a novel Hebrew-language corpus of 10,410 sentences drawn from Knesset speeches (1993-2023), Facebook posts (2018-2021), and leading news outlets, of which 1,812 instances (17.4\%) exhibit PDD and 642 carry additional annotations for intensity, incivility, target type, and affective framing. We introduce a two-stage classification pipeline combining finetuned encoder models and decoder LLMs. Our best model (DictaLM 2.0) attains an F₁ of 0.74 for binary PDD detection and a macro-F₁ of 0.67 for classification of delegitimization characteristics. Applying this classifier to longitudinal and cross-platform data, we see a marked rise in PDD over three decades, higher prevalence on social media versus parliamentary debate, greater use by male than female politicians, and stronger tendencies among right-leaning actors - with pronounced spikes during election campaigns and major political events. Our findings demonstrate the feasibility and value of automated PDD analysis for understanding democratic discourse.

The Enemy from Within: A Study of Political Delegitimization Discourse in Israeli Political Speech

During spontaneous conversations, speakers collaborate on novel referring expressions, which they can then re-use in subsequent conversations. Understanding such referring expressions is an important ability for an embodied agent, so that it can carry out tasks in the real world. This requires integrating and understanding language, vision, and conversational interaction. We study the capabilities of seven state-of-the-art Large Vision Language Models (LVLMs) as overhearers to a corpus of spontaneous conversations between pairs of human discourse participants engaged in a collaborative object-matching task. We find that such a task remains challenging for current LVLMs and they all fail to show a consistent performance improvement as they overhear more conversations from the same discourse participants repeating the same task for multiple rounds. We release our corpus for reproducibility.

LVLMs are Bad at Overhearing Human Referential Communication

Despite biases in Large Language Models (LLMs) being widely researched, systematic explorations of political biases in news article generation tasks remain underexplored. This study evaluates political bias across seven LLMs by leveraging our PublicViews dataset-extracted from the TwinViews-13K corpus-comprising 31 topics and 31,692 statements. We analyze 10,850 articles, finding left-leaning bias persists in generation tasks, with neutral content remaining rare even under balanced opinion settings. Models exhibit asymmetric behavior in minority opinion scenarios, amplifying preferred viewpoints when in minority while conforming to majority opinions otherwise. Notably, all models employ `stance-flipping quotations'' (altering supporters' statements to express opposite viewpoints) in 33-38\% of quotations despite explicit instructions against distortion. Consistent with prior research, increased model size failed to enhance neutrality. This research measures political bias in LLM-generated news, analyzes its mechanisms, and reveals how opinion distribution and explicitness affect bias expression. Our results highlight how LLMs can introduce unintended political bias in generative contexts. We publicly release our PublicViews corpus and code at https://anonymous.4open.science/r/Fair-or-Framed-46F1.

Fair or Framed? Political Bias in News Articles Generated by LLMs

Unlike traditional search engines that present ranked lists of webpages, generative search engines rely solely on in-line citations as the key gateway to real-world information exposure, making it crucial to examine whether LLM-based citation generation introduces bias—particularly for politically sensitive queries. In this work, we introduce AllSides-2024, a newly constructed dataset comprising the latest real-world news articles labeled with left- or right-leaning stances for investigating potential political bias in LLM-generated citations. Through systematic evaluations, we find that LLMs exhibit a consistent tendency to cite left-leaning sources at notably higher rates compared to traditional retrieval systems. Controlled experiments further reveal that this bias arises from a preference for media outlets identified as left-leaning, rather than for left-oriented content itself. Meanwhile, our findings show that while LLMs struggle to infer political bias from news content alone, they can almost perfectly recognize the political orientation of media outlets based on their names. These insights highlight the risk that, in the era of generative search engines, information exposure may be disproportionately shaped by specific media outlets, further shaping public perception and decision-making.

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KLAAD: Refining Attention Mechanisms to Reduce Societal Bias in Generative Language Models

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