China

Role-Playing Language Agents (RPLAs) have emerged as a significant application direction for Large Language Models (LLMs). Existing approaches typically rely on prompt engineering or supervised fine-tuning to enable models to imitate character behaviors in specific scenarios, but often neglect the underlying cognitive mechanisms driving these behaviors. Inspired by cognitive psychology, we introduce CogDual, a novel RPLA adopting a cognize-then-respond reasoning paradigm. By jointly modeling external situational awareness and internal self-awareness, CogDual generates responses with improved character consistency and contextual alignment. To further optimize the performance, we employ reinforcement learning with two general-purpose reward schemes designed for open-domain text generation. Extensive experiments on the CoSER benchmark, as well as Cross-MR and LifeChoice, demonstrate that CogDual consistently outperforms existing baselines and generalizes effectively across diverse role-playing tasks.

EMNLP 2025

CogDual: Enhancing Dual Cognition of LLMs via Reinforcement Learning with Implicit Rule-Based Rewards

cognitive modeling; role-play; reinforcement learning; dialogue

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*

[**Link to Conference Handbook**](https://drive.google.com/file/d/1johU5QqVVYO4RfH7QcIORr7qrVBdzdwC/view?usp=sharing)





<br>

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

Achieving human-level translations requires leveraging context to ensure coherence and handle complex phenomena like pronoun disambiguation. Sparsity of contextually rich examples in the standard training data has been hypothesized as the reason for the difficulty of context utilization. In this work, we systematically validate this claim in both single- and multilingual settings by constructing training datasets with a controlled proportions of contextually relevant examples. We demonstrate a strong association between training data sparsity and model performance confirming sparsity as a key bottleneck. Importantly, we reveal that improvements in one contextual phenomenon do no generalize to others. While we observe some cross-lingual transfer, it is not significantly higher between languages within the same sub-family. Finally, we propose and empirically evaluate two training strategies designed to leverage the available data. These strategies improve context utilization, resulting in accuracy gains of up to 6 and 8 percentage points on the ctxPro evaluation in single- and multilingual settings respectively.

You Are What You Train: Effects of Data Composition on Training Context-aware Machine Translation Models

Certifying the robustness of Deep Neural Networks (DNNs) is crucial, especially with the rise of powerful generative models, such as Large Language Models (LLMs) or Vision-Language Models (VLMs), that have the potential of generating dangerous or harmful responses. Recent work has shown that these large-scale models are still susceptible to adversarial attacks, despite their safety fine-tuning. Randomized Smoothing (RS), the current state-of-the-art (SoTA) method for robustness certification, cannot be applied on models such as VLMs: first, RS is designed for classification, not generation. Second, RS is a probabilistic approach, typically requiring 10^5 samples to certify a single input, making it infeasible for large-scale modern VLMs. This is the challenge we aim to solve in this paper. First, we reformulate RS for the case of generative models, where we distinguish between harmless and harmful responses. Moreover, we develop a theory that allows us to reduce the number of samples required by 2-3 orders of magnitude, without much effect on the certified radius, and mathematically analyze its dependence to the number of samples. Combined, these advances allow us to scale RS on state-of-the-art VLMs, something that was not feasible before. We successfully showcase this experimentally by defending against a recent SoTA attack against aligned VLMs.

Randomized Smoothing Meets Vision-Language Models

Recent advancements in large language models (LLMs) have shifted focus toward scaling inference-time compute—improving performance without retraining the model. A common approach is to sample multiple outputs in parallel, and select one of these as the final output. This was shown to boost output quality in multiple settings for English. However, the question remains about how to best apply these methods across diverse languages and tasks. In this work, we study how to robustly scale inference-time compute for open-ended generative tasks in a multilingual, multi-task setting. Our findings show that both sampling strategy---based on temperature variation---and selection strategy must be adapted to account for language-specific characteristics. We evaluate existing and novel selection methods, revealing that strategies effective in English often fail to generalize across languages. Our results underscore the need for language- and task-aware approaches to inference-time compute, aiming to democratize performance improvements in underrepresented languages.

When Life Gives You Samples: The Benefits of Scaling up Inference Compute for Multilingual LLMs

Ambiguity is pervasive in language, yet we resolve it effortlessly and unconsciously, often aided by context and part-of-speech (POS) cues. This study investigates how context similarity and POS influence homonym disambiguation in humans and large language models (LLMs). To enable comparable analyses between humans and LLMs, we first built an expert-curated sentence-pair dataset, manipulating context similarity and homonym POS categories. Participants (n = 55) and LLMs (via prompting) were asked to rate the sense similarity of target homonyms embedded within each sentence on a 7-point Likert scale. We found that context similarity influenced both groups similarly, but only humans utilized POS information, likely contributing to their superior performance. Model-derived metrics (surprisal, entropy) predicted human reaction times, and angular similarity between homonym representations accounted for additional variance, highlighting the roles of both expectation-based and semantic processes. Psycholinguistic factors like age of acquisition affected only human responses, underscoring distinct language acquisition mechanisms. Together, our findings illustrate how context and POS information interactively shape homonym resolution in humans, while exposing the limitations of current language models in capturing these nuanced processes. Dataset and codes are publicly available at https://anonymous.4open.science/r/context-and-pos-in-action-976D.

Context and POS in Action: A Comparative Study of Chinese Homonym Disambiguation in Human and Language Models

We investigate the potential of LLM-generated synthetic data for improving low-resource machine translation (MT). Focusing on seven diverse target languages, we construct a document-level synthetic corpus from English Europarl, and extend it via pivoting to 147 additional language pairs. Automatic and human evaluation confirm its high overall quality. We study its practical application by (i) identifying effective training regimes, (ii) comparing our data with the HPLT dataset, and (iii) testing its utility beyond English-centric MT. Finally, we introduce [ANON], a public repository for synthetic parallel datasets. Our findings show that LLM-generated synthetic data, even when noisy, can substantially improve MT performance for low-resource languages.

Scaling Low-Resource MT via Synthetic Data Generation with LLMs

With over 2,000 languages and potentially millions of speakers, Africa represents one of the richest linguistic regions in the world. Yet, this diversity is scarcely reflected in state-of-the-art natural language processing (NLP) systems and large language models (LLMs), which predominantly support a narrow set of high-resource languages. This exclusion not only limits the reach and utility of modern NLP technologies but also risks widening the digital divide across linguistic communities. Nevertheless, NLP research on African languages is active and growing. In recent years, there has been a surge of interest in this area, driven by several factors—including the creation of multilingual language resources, the rise of community-led initiatives, and increased support through funding programs. In this survey, we analyze 735 research papers on NLP for African languages published over the past five years, offering a comprehensive overview of recent progress across core tasks. We identify key trends shaping the field and conclude by outlining promising directions to foster more inclusive and sustainable NLP research for African languages.

Charting the Landscape of African NLP: Mapping Progress and Shaping the Road Ahead

Pretrained multilingual models exhibit the same social bias as models processing English texts. This systematic review analyzes emerging research that extends bias evaluation and mitigation approaches into multilingual and non-English contexts. We examine these studies with respect to linguistic diversity, cultural awareness, and their choice of evaluation metrics and mitigation techniques. Our survey illuminates gaps in the field’s dominant methodological design choices (e.g., preference for certain languages, scarcity of multilingual mitigation experiments) while cataloging common issues encountered and solutions implemented in adapting bias benchmarks across languages and cultures. Drawing from the implications of our findings, we chart directions for future research that can reinforce the multilingual bias literature’s inclusivity, cross-cultural appropriateness, and alignment with state-of-the-art NLP advancements.

Social Bias in Multilingual Language Models: A Survey

Self-supervised speech models can be trained to efficiently recognize spoken words in naturalistic, noisy environments. However, we do not understand the types of linguistic representations these models use to accomplish this task. To address this question, we study how S3M variants optimized for word recognition represent phonological and morphological phenomena in frequent English noun and verb inflections. We find that their representations exhibit a global linear geometry which can be used to link English nouns and verbs to their regular inflected forms. This geometric structure does not directly track phonological or morphological units. Instead, it tracks the regular distributional relationships linking many word pairs in the English lexicon—often, but not always, due to morphological inflection. These findings point to candidate representational strategies that may support human spoken word recognition, challenging the presumed necessity of distinct linguistic representations of phonology and morphology.

Emergent morpho-phonological representations in self-supervised speech models

Large Language Models (LLMs) are powerful tools with profound societal impacts, yet their ability to generate responses to diverse and uncontrolled inputs leaves them vulnerable to adversarial attacks. While existing defenses often struggle to generalize across varying attack types, recent advancements in representation engineering offer promising alternatives. In this work, we propose a defense framework that formulates model defense as a contrastive representation learning (CRL) problem. Our method finetunes a model using a triplet-based loss combined with adversarial hard negative mining to encourage separation between benign and harmful representations. Our experimental results across multiple models demonstrate that our approach outperforms prior representation engineering-based defenses, improving robustness against both input-level and embedding-space attacks without compromising standard performance.

Improving Large Language Model Safety with Contrastive Representation Learning

Temporal Domain Generalization (TDG) aim at generalizing across temporal distribution shifts, e.g., lexical change over time, by predicting future models. Due to the prohibitive full model prediction cost on large-scale scenarios, recent TDG works only predict the classifier, but this limits generalization potential by failing to adjust other model components. To address this, we propose Temporal Experts Averaging (TEA), a novel TDG framework based on weight averaging that adjusts the entire model to maximize generalization potential while maintaining minimal computational overhead when scaling to large-scale datasets and models. Our theoretical analysis of weight averaging for TDG guided us to develop two steps that enhance generalization to future domains. First, we create expert models with functional diversity yet parameter similarity by fine-tuning a domain-agnostic base model on individual temporal domains while constraining weight changes. Second, we optimize the bias-variance tradeoff through adaptive averaging coefficients derived from modeling temporal weight trajectories in a principal component subspace and weighting experts based on their projected proximity to future domains in the subspace. Extensive experiments across 7 TDG benchmarks, 5 models, and 2 TDG settings reports TEA outperforms prior TDG methods by up to 69% while being up to 60x more efficient.

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You Are What You Train: Effects of Data Composition on Training Context-aware Machine Translation Models

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