China

Large language models (LLMs) have shown remarkable capabilities in general domains, but their application to multi-omics biology remains underexplored. To address this gap, we introduce Biology-Instructions, the first large-scale instruction-tuning dataset for multi-omics biological sequences, including DNA, RNA, proteins, and multi-molecules. This dataset bridges LLMs and complex biological sequences-related tasks, enhancing their versatility and reasoning while maintaining conversational fluency. We also highlight significant limitations of current state-of-the-art LLMs on multi-omics tasks without specialized training. To overcome this, we propose ChatMultiOmics, a strong baseline with a novel three-stage training pipeline, demonstrating superior biological understanding through Biology-Instructions. Both resources are publicly available, paving the way for better integration of LLMs in multi-omics analysis. The Biology-Instructions is publicly available at \href{https://anonymous.4open.science/r/Biology-Instructions-7D86/}{link}.

EMNLP 2025

Biology-Instructions: A Dataset and Benchmark for Multi-Omics Sequence Understanding Capability of Large Language Models

ai for biology

instruction benchmark

multi-omics

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

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AAAI 2026

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

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." 

*Message from the General Chair, Dirk Hovy*

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

Large language models (LLMs) have advanced the automation of data science workflows. Yet it remains unclear whether they can critically leverage external domain knowledge as human data scientists do in practice. To answer this question, we introduce AssistedDS (Assisted Data Science), a benchmark designed to systematically evaluate how LLMs handle domain knowledge in tabular prediction tasks. AssistedDS features both synthetic datasets with explicitly known generative mechanisms and real-world Kaggle competitions, each accompanied by curated bundles of helpful and adversarial documents. These documents provide domain-specific insights into data cleaning, feature engineering, and model selection. We assess state-of-the-art LLMs on their ability to discern and apply beneficial versus harmful domain knowledge, evaluating submission validity, information recall, and predictive performance. Our results demonstrate three key findings: (1) LLMs frequently exhibit an uncritical adoption of provided information, significantly impairing their predictive performance when adversarial content is introduced, (2) helpful guidance is often insufficient to counteract the negative influence of adversarial information, and (3) in Kaggle datasets, LLMs often make errors in handling time-series data, applying consistent feature engineering across different folds, and interpreting categorical variables correctly. These findings highlight a substantial gap in current models' ability to critically evaluate and leverage expert knowledge, underscoring an essential research direction for developing more robust, knowledge-aware automated data science systems.

AssistedDS: Benchmarking How External Domain Knowledge Assists LLMs in Automated Data Science

Automatic content moderation is crucial to ensuring safety in social media. Language Model-based classifiers are increasingly adopted for this task, but it has been shown that they perpetuate racial and social biases. Even if several resources and benchmark corpora have been developed to challenge this issue, measuring the fairness of models in content moderation remains an open issue. In this work, we present an unsupervised approach that benchmarks models on the basis of their uncertainty in classifying messages annotated by people belonging to vulnerable groups. We use uncertainty, computed by means of the conformal prediction technique, as a proxy to analyze the bias of 11 models (LMs and LLMs) against women and non-white annotators and observe to what extent it diverges from metrics based on performance, such as the F1 score. The results show that some pre-trained models predict with high accuracy the labels coming from minority groups, even if the confidence in their prediction is low. Therefore, by measuring the confidence of models, we are able to see which groups of annotators are better represented in pre-trained models and lead the debiasing process of these models before their effective use.

Are you sure? Measuring models bias in content moderation through uncertainty

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.

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

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.

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AssistedDS: Benchmarking How External Domain Knowledge Assists LLMs in Automated Data Science

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