China

Online Mental Health Communities (OMHCs) provide crucial peer and expert support, yet many posts remain unanswered due to missing support attributes that signal the need for help. We present a novel framework that identifies these gaps and prompts users to enrich their posts, thereby improving engagement. To support this, we introduce REDDME, a new dataset of 4,760 posts from mental health subreddits annotated for the span and intensity of three key support attributes: event what happened?, effect what did the user experience?, and requirement what support they need?. Next, we devise a hierarchical taxonomy, CueTaxo, of support attributes for controlled question generation. Further, we propose MH-COPILOT, a reinforcement learning-based system that integrates (a) contextual attribute-span identification, (b) support attribute intensity classification, (c) controlled question generation via a hierarchical taxonomy, and (d) a verifier for reward modeling. Our model dynamically assesses posts for the presence/absence of support attributes, and generates targeted prompts to elicit missing information. Empirical results across four notable language models demonstrate significant improvements in attribute elicitation and user engagement. A human evaluation further validates the model&#39;s effectiveness in real-world OMHC settings.

EMNLP 2025

Assess and Prompt: A Generative RL Framework for Improving Engagement in Online Mental Health Communities

Online Mental Health Communities (OMHCs) provide crucial peer and expert support, yet many posts remain unanswered due to missing support attributes that signal the need for help. We present a novel framework that identifies these gaps and prompts users to enrich their posts, thereby improving engagement. To support this, we introduce REDDME, a new dataset of 4,760 posts from mental health subreddits annotated for the span and intensity of three key support attributes: event what happened?, effect what did the user experience?, and requirement what support they need?. Next, we devise a hierarchical taxonomy, CueTaxo, of support attributes for controlled question generation. Further, we propose MH-COPILOT, a reinforcement learning-based system that integrates (a) contextual attribute-span identification, (b) support attribute intensity classification, (c) controlled question generation via a hierarchical taxonomy, and (d) a verifier for reward modeling. Our model dynamically assesses posts for the presence/absence of support attributes, and generates targeted prompts to elicit missing information. Empirical results across four notable language models demonstrate significant improvements in attribute elicitation and user engagement. A human evaluation further validates the model's effectiveness in real-world OMHC settings.

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.

Retrieval-Augmented Generation (RAG) is widely adopted for its effectiveness and cost-efficiency in mitigating hallucinations and enhancing the domain-specific generation capabilities of large language models (LLMs). However, is this effectiveness and cost-efficiency truly a free lunch? In this study, we comprehensively investigate the fairness costs associated with RAG by proposing a practical three-level threat model from the perspective of user awareness of fairness. Specifically, varying levels of user fairness awareness result in different degrees of fairness censorship on external datasets. We examine the fairness implications of RAG using uncensored, partially censored, and fully censored datasets. Our experiments demonstrate that fairness alignment can be easily undermined through RAG without the need for fine-tuning or retraining. Even with fully censored and supposedly unbiased external datasets, RAG would still lead to biased outputs. Our findings underscore the limitations of current alignment methods in the context of RAG-based LLMs and highlight the urgent need for new strategies to ensure fairness. We propose potential mitigations and call for further research to develop robust fairness safeguards in RAG-based LLMs.

No Free Lunch: Retrieval-Augmented Generation Undermines Fairness in LLMs, Even for Vigilant Users

Humans have an inherent need for community belonging. This paper investigates this fundamental social motivation by compiling a large collection of parallel datasets comprising over 7 million posts and comments from Reddit and 200,000 posts and comments on Dread, a dark web discussion forum, covering similar topics. Through five aspects grounded in the sense of community theory, our analysis indicates that users on Dread exhibit a stronger sense of community. Our data analysis reveals striking similarities in post content across both platforms, despite the dark web's restricted accessibility. However, these communities differ significantly in community-level closeness, including member interactions and greeting patterns, influencing user retention and dynamics. We publicly release the parallel community datasets, enabling future research to examine key differences and explore potential directions for further study.

Parallel Communities Across the Surface Web and the Dark Web

Old Abstract: Sign Language Translation has advanced with deep learning, yet evaluations remain signer-dependent, with overlapping signers in training, development, and test sets. This raises concerns about whether models truly generalise or rely on signer-specific features. To address this, signer-fold cross-validation is conducted on GFSLT-VLP, GASLT, and SignCL—three leading, publicly available, non-proprietary gloss-free sign language translation models, with SignCL being among the most prominent. Experiments are performed on two benchmarking datasets, CSL-Daily and PHOENIX14T. The results reveal a significant performance drop under signer-independent settings. On PHOENIX14T, GFSLT-VLP sees BLEU-4 fall from 21.44 to as low as 3.59 and ROUGE-L from 42.49 to 11.89; GASLT drops from a reported 15.74 to 8.26; and SignCL from 22.74 to 3.66. These findings highlight the substantial overestimation of SLT model performance when evaluations are conducted under signer-dependent assumptions. This work proposes two key recommendations: (1) adopting signer-independent evaluation protocols, and (2) restructuring datasets to include signer-independent splits. Updated Abstract: Sign Language Translation has advanced with deep learning, yet evaluations remain signer-dependent, with overlapping signers in training, development, and test sets. This raises concerns about whether models truly generalise or rely on signer-specific features. To address this, signer-fold cross-validation is conducted on GFSLT-VLP, GASLT, and SignCL—three leading, publicly available, non-proprietary gloss-free sign language translation models. Experiments are performed on two benchmarking datasets, CSL-Daily and PHOENIX14T. The results reveal a significant performance drop under signer-independent settings. On PHOENIX14T, GFSLT-VLP sees BLEU-4 fall from 21.44 to as low as 3.59 and ROUGE-L from 42.49 to 11.89; GASLT drops from a reported 15.74 to 8.26; and SignCL from 22.74 to 3.66. Similarly, on CSL-Daily, GASLT’s BLEU-4 drops from 4.07 to an average of 3.63 under signer-fold cross-validation, despite the increased training data. These findings highlight the substantial overestimation of SLT model performance when evaluations are conducted under signer-dependent assumptions. This work proposes three key recommendations: (1) adopting signer-independent evaluation protocols to ensure generalisation to unseen signers, (2) restructuring existing datasets to include explicit signer-independent splits for consistent benchmarking, and (3) encouraging the reporting of both signer-dependent and signer-independent results to improve transparency and comparability.

Rethinking Sign Language Translation: The Impact of Signer Dependence on Model Evaluation

Suicide remains a major global mental health challenge, and early intervention hinges on recognizing signs of suicidal ideation. In private conversations, such ideation is often expressed in subtle or conflicted ways, making detection especially difficult. Existing data sets are mainly based on public help-seeking platforms such as Reddit, which fail to capture the introspective and ambiguous nature of suicidal ideation in more private contexts. To address this gap, we introduce \ourdata, a novel dataset of 1,200 test cases simulating implicit suicidal ideation within psychologically rich dialogue scenarios. Each case is grounded in psychological theory, combining the Death/Suicide Implicit Association Test (D/S-IAT) patterns, expanded suicidal expressions, cognitive distortions, and contextual stressors. In addition, we propose a psychology-guided evaluation framework to assess the ability of LLMs to identify implicit suicidal ideation through their responses. Experiments with eight widely used LLMs across varied prompting conditions reveal that current models often struggle significantly to recognize implicit suicidal ideation. Our findings highlight the urgent need for more clinically grounded evaluation frameworks and design practices to ensure the safe use of LLMs in sensitive support systems.

Can Large Language Models Identify Implicit Suicidal Ideation? An Empirical Evaluation

We present a novel three-stage framework leveraging Large Language Models (LLMs) within a risk-aware multi-agent system for automate strategy finding in quantitative finance. Our approach addresses the brittleness of traditional deep learning models in financial applications by: employing prompt-engineered LLMs to generate executable alpha factor candidates across diverse financial data, implementing multimodal agent-based evaluation that filters factors based on market status, predictive quality while maintaining category balance, and deploying dynamic weight optimization that adapts to market conditions. Experimental results demonstrate the robust performance of the strategy in Chinese & US market regimes compared to established benchmarks. Our work extends LLMs capabilities to quantitative trading, providing a scalable architecture for financial signal extraction and portfolio construction. The overall framework significantly outperforms all benchmarks with 53.17% cumulative return on SSE50 (Jan 2023 to Jan 2024), demonstrating superior risk-adjusted performance and downside protection on the market.

Automate Strategy Finding with LLM in Quant Investment

While Large Language Models (LLMs) excel at temporal reasoning tasks like event ordering and duration estimation, their ability to perceive the actual passage of time remains unexplored. We investigate whether LLMs perceive the passage of time and adapt their decision-making accordingly through three complementary experiments. First, we introduce the Token-Time Hypothesis, positing that LLMs can map discrete token counts to continuous wall-clock time, and validate this through a dialogue duration judgment task. Second, we demonstrate that LLMs could use this awareness to adapt their response length while maintaining accuracy when users express urgency in question answering tasks. Finally, we develop BombRush, an interactive navigation challenge that examines how LLMs modify behavior under progressive time pressure in dynamic environments. Our findings indicate that LLMs possess certain awareness of time passage, enabling them to bridge discrete linguistic tokens and continuous physical time, though this capability varies with model size and reasoning abilities. This work establishes a theoretical foundation for enhancing temporal awareness in LLMs for time-sensitive applications.

Discrete Minds in a Continuous World: Do Language Models Know Time Passes?

The unlearning method aims at effectively removing harmful, sensitive, or outdated knowledge without costly retraining the model. However, existing methods suffer from two critical limitations: (1) collateral forgetting, where erasing target data inadvertently removes related but desirable knowledge, and (2) generality forgetting, where aggressive unlearning degrades the model's general capabilities. To address these challenges, we propose DirectiOn Guide unlEarning (DOGE), a novel method that enables precise knowledge erasure by identifying and leveraging a targeted "unlearning direction" in the model’s parameter space. DOGE first extracts this direction through differential analysis of representations for forgotten and retained samples, pinpointing the exact subspace associated with unwanted knowledge. It then selectively applies updates along this direction, ensuring minimal interference with retained information and general model performance. Experiments across multiple benchmarks demonstrate that Doge achieves state-of-the-art unlearning precision while preserving both related knowledge and general capabilities.

Lock on Target! Precision Unlearning via Directional Control

Large Language Models (LLMs) enhanced with Retrieval-Augmented Generation (RAG) have shown improved performance in generating accurate responses. However, the dependence on external knowledge bases introduces potential security vulnerabilities, particularly when these knowledge bases are publicly accessible and modifiable. While previous studies have exposed knowledge poisoning risks in RAG systems, existing attack methods suffer from critical limitations: they either require injecting multiple poisoned documents (resulting in poor stealthiness) or can only function effectively on simplistic queries (limiting real-world applicability). This paper reveals a more realistic knowledge poisoning attack against RAG systems that achieves successful attacks by poisoning only a single document while remaining effective for complex multi-hop questions involving complex relationships between multiple elements. Our proposed AuthChain address three challenges to ensure the poisoned documents are reliably retrieved and trusted by the LLM, even against large knowledge bases and LLM's own knowledge. Extensive experiments across six popular LLMs demonstrate that AuthChain achieves significantly higher attack success rates while maintaining superior stealthiness against RAG defense mechanisms compared to state-of-the-art baselines.

One Shot Dominance: Knowledge Poisoning Attack on Retrieval-Augmented Generation Systems

Robustness evaluation applied to linguistic variation is key to understanding the generalization capabilities of Natural Language to SQL (NL2SQL) models, yet existing benchmarks seldom address this factor in a systematic or controlled manner. We propose a novel schema-aligned paraphrasing framework that leverages SQL-to-NL (SQL2NL) to automatically generate semantically equivalent, lexically diverse queries while maintaining alignment with the original schema and intent. This enables the first targeted evaluation of NL2SQL robustness to linguistic variation in isolation---distinct from prior work that primarily investigates ambiguity or schema perturbations. Our analysis reveals that state-of-the-art models are far more brittle than standard benchmarks suggest. For example, LLaMa3.3-70B exhibits a 10.23% drop in execution accuracy (from 77.11% to 66.9%) on paraphrased Spider queries, while LLaMa3.1-8B suffers an even larger drop of nearly 20% (from 62.9% to 42.5%). Smaller models are disproportionately affected. We also find that robustness degradation varies significantly with query complexity, dataset, and domain---highlighting the need for evaluation frameworks that explicitly measure linguistic generalization to ensure reliable performance in real-world settings.

Evaluating NL2SQL via SQL2NL

As large language models (LLMs) are increasingly applied across various domains, enhancing safety while maintaining the helpfulness of LLMs has become a critical challenge. Recent studies solve this problem through safety-constrained online preference optimization or safety-constrained offline preference optimization. However, the safety-constrained online methods often suffer from excessive safety, which might reduce helpfulness, while the safety-constrained offline methods perform poorly in adaptively balancing safety and helpfulness. To address these limitations, we propose MidPO, a Mixture of Experts (MoE) framework for safety-helpfulness dual Preference Optimization. Firstly, MidPO devises single-preference enhanced direct preference optimization approach to transform the base model into two independent experts, termed safety and helpfulness experts, and fine-tunes the two independent experts for optimal safety or helpfulness performance. Secondly, to achieve an effective balance between safety and helpfulness, MidPO incorporates the two experts into the MoE framework and designs a dynamic routing mechanism to allocate contributions from each expert adaptively. We conduct quantitative and qualitative experiments on three popular datasets to demonstrate the proposed MidPO significantly outperforms state-of-the-art approaches in both safety and helpfulness. Code is available at https://shorturl.at/BmNa8.

Downloads

Next from EMNLP 2025

No Free Lunch: Retrieval-Augmented Generation Undermines Fairness in LLMs, Even for Vigilant Users

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES