China

Large Language Models (LLMs) as clinical agents require careful behavioral adaptation. While adept at reactive tasks (e.g., diagnosis reasoning), LLMs often struggle with proactive engagement, like unprompted identification of critical missing information or risks. We introduce **BehaviorBench**, a comprehensive dataset to evaluate agent behaviors across a clinical assistance spectrum, ranging from reactive query responses to proactive interventions (e.g., clarifying ambiguities, flagging overlooked critical data). Our BehaviorBench experiments reveal LLMs&#39; inconsistent proactivity. To address this, we propose **BehaviorSFT**, a novel training strategy using behavioral tokens to explicitly condition LLMs for dynamic behavioral selection along this spectrum. BehaviorSFT boosts performance, achieving up to 97.3% overall Macro F1 on BehaviorBench and improving proactive task scores (e.g., from 95.0% to 96.5% for Qwen2.5-7B-Ins). Crucially, blind clinician evaluations confirmed BehaviorSFT-trained agents exhibit more realistic clinical behavior, striking a superior balance between helpful proactivity (e.g., timely, relevant suggestions) and necessary restraint (e.g., avoiding over-intervention) versus standard fine-tuning or explicit instructed agents.

EMNLP 2025

BehaviorSFT: Behavioral Token Conditioning for Health Agents Across the Proactivity Spectrum

health agent

behaviorsft

proactive agent

## Welcome to EMNLP 2024! 
We are excited to welcome you to one of the most prominent conferences in the field of Natural Language Processing. This year, EMNLP 2024 is being held in a hybrid format,
offering both virtual and in-person participation in beautiful Miami. Due to a record-breaking number of submissions, we've expanded the total number of accepted papers to accommodate more cutting-edge research from around the globe.
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EMNLP 2024

EMNLP 2024 will take place in Miami, Florida from Nov 12th to Nov 16th, 2024, at the Hyatt Regency Miami Hote and on Underline for remote participants.

Large Language Models (LLMs) as clinical agents require careful behavioral adaptation. While adept at reactive tasks (e.g., diagnosis reasoning), LLMs often struggle with proactive engagement, like unprompted identification of critical missing information or risks. We introduce **BehaviorBench**, a comprehensive dataset to evaluate agent behaviors across a clinical assistance spectrum, ranging from reactive query responses to proactive interventions (e.g., clarifying ambiguities, flagging overlooked critical data). Our BehaviorBench experiments reveal LLMs' inconsistent proactivity. To address this, we propose **BehaviorSFT**, a novel training strategy using behavioral tokens to explicitly condition LLMs for dynamic behavioral selection along this spectrum. BehaviorSFT boosts performance, achieving up to 97.3% overall Macro F1 on BehaviorBench and improving proactive task scores (e.g., from 95.0% to 96.5% for Qwen2.5-7B-Ins). Crucially, blind clinician evaluations confirmed BehaviorSFT-trained agents exhibit more realistic clinical behavior, striking a superior balance between helpful proactivity (e.g., timely, relevant suggestions) and necessary restraint (e.g., avoiding over-intervention) versus standard fine-tuning or explicit instructed agents.

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.

Distractor generation is the task of automatically generating plausible yet incorrect options (i.e., distractors) for fill-in-the-blank and multiple-choice questions. In assessment, distractors must be contextually relevant to the given question and answer. Even though recent research works focus on fine-tuning pre-trained encoder-decoder models with data augmentation techniques to generate distractors, these models often fail to capture the full semantic representation of a given question-answer and related distractors. The augmentation methods often rely on expanding the quantity of proposed candidates (i.e., questions or distractors), which can introduce noise into the models without necessarily enhancing their understanding of the deeper semantic relationships between question-answer and related distractors. This paper introduces a novel distractor generation model based on contrastive learning to train the model to recognize essential semantic features necessary to generate in-context distractors. The extensive experiments on two public datasets indicate that contrastive learning introduces a strong baseline model to the distractor generation task. It significantly outperforms recent models, increasing the NDCG@3 score from 24.68 to 32.33 on the MCQ dataset and from 26.66 to 36.68 on the SciQ dataset.

Fine-Tuning Encoder-Decoder Models with Contrastive Learning for In-Context Distractor Generation

As NLP models become increasingly integrated into real-world applications, it becomes clear that there is a need to address the fact that models often rely on and generate conflicting information. Conflicts could reflect the complexity of situations, changes that need to be explained and dealt with, difficulties in data annotation, and mistakes in generated outputs. In all cases, disregarding the conflicts in data could result in undesired behaviors of models and undermine NLP models’ reliability and trustworthiness. This survey categorizes these conflicts into three key areas: (1) natural texts on the web, where factual inconsistencies, subjective biases, and multiple perspectives introduce contradictions; (2) human-annotated data, where annotator disagreements, mistakes, and societal biases impact model training; and (3) model interactions, where hallucinations and knowledge conflicts emerge during deployment. While prior work has addressed some of these conflicts in isolation, we unify them under the broader concept of conflicting information, analyze their implications, and discuss mitigation strategies. We highlight key challenges and future directions for developing conflict-aware NLP systems that can reason over and reconcile conflicting information more effectively.

Conflicts in Texts: Data, Implications and Challenges

Generating radiology reports from medical images has garnered sufficient attention in the research community. While existing methods have demonstrated promise, they often tend to generate reports that are factually incomplete and inconsistent, fail to focus on informative regions within an image, and impose strong annotation assumptions, such as bounding box annotations, image level annotations (which can be challenging to obtain) for model training. In this paper, we propose MediVLM, a vision language model (VLM) for radiology report generation from medical images. The proposed model consists of a pre-trained object detector to extract the salient anatomical regions from the images, an image encoder, a text encoder, a module to align the visual and text representations, a cross attention layer to fuse the two representations and finally, a transformer based decoder to generate the final report. MediVLM can generate radiology reports even when no reports are available for training; this is an extremely useful feature, as curating such reports is a labor-intensive task. Further, it computes a severity score (depicting the seriousness of a patient's medical condition) from the generated radiology reports, which can be used to prioritize patients who need immediate medical attention. Our extensive empirical analyses on three benchmark datasets corroborate the promise and potential of our method against competing baselines.

MediVLM: A Vision Language Model for Radiology Report Generation from Medical Images

Designing polymers for targeted applications and accurately predicting their properties is a key challenge in materials science owing to the vast and complex polymer chemical space. While molecular language models have proven effective in solving analogous problems for molecular discovery, similar advancements for polymers are limited. To address this gap, we propose polyBART, a language model-driven polymer discovery capability that enables rapid and accurate exploration of the polymer design space. Central to our approach is Pseudo-polymer SELFIES (PSELFIES), a novel representation that allows for the transfer of molecular language models to the polymer space. polyBART is, to the best of our knowledge, the first language model capable of bidirectional translation between polymer structures and properties, achieving state-of-the-art results in property prediction and design of novel polymers for electrostatic energy storage. Further, polyBART is validated through a combination of both computational and laboratory experiments. We report what we believe is the first successful synthesis and validation of a polymer designed by a language model, predicted to exhibit high thermal degradation temperature and confirmed by our laboratory measurements. Our work presents a generalizable strategy for adapting molecular language models to the polymer space and introduces a polymer foundation model, advancing generative polymer design that may be adapted for a variety of applications.

polyBART: A Chemical Linguist for Polymer Property Prediction and Generative Design

Position bias, where Large Language Models (LLMs) overrepresent content from the beginnings and endings of documents while neglecting middle sections, has been considered a core limitation in automatic summarization. To measure position bias, prior studies have commonly relied on n-gram matching techniques, which can miss semantic relationships in abstractive summaries where content is extensively rephrased. To address this limitation, we apply a cross-encoder-based alignment method that jointly processes summary–source sentence pairs, enabling more accurate identification of semantic correspondences, even when summaries substantially rewrite the source. Experiments with five LLMs across six summarization datasets reveal markedly different position bias patterns than those reported by traditional metrics. Our findings suggest that these biases primarily reflect rational adaptations to document structure and content rather than true model limitations. Through controlled experiments and analyses across varying document lengths and multi-document settings, we show that LLMs utilize content from all positions more effectively than previously assumed, challenging common claims about “lost-in-the-middle” behaviour.

Not Lost After All: How Cross-Encoder Attribution Challenges Position Bias Assumptions in LLM Summarization

Handwritten text recognition aims to convert visual input into machine-readable text, and it remains challenging due to the evolving and context-dependent nature of handwriting. Character sets change over time, and character frequency distributions shift across historical periods or regions, often causing models trained on broad, heterogeneous corpora to underperform on specific subsets. To tackle this, we propose a novel loss function that incorporates the Wasserstein distance between the character frequency distribution of the predicted text and a target distribution empirically derived from training data. By penalizing divergence from expected distributions, our approach enhances both accuracy and robustness under temporal and contextual intra-dataset shifts. Furthermore, we demonstrate that character distribution alignment can also improve existing models at inference time without requiring retraining by integrating it as a scoring function in a guided decoding scheme. Experimental results across multiple datasets and architectures confirm the effectiveness of our method in boosting generalization and performance.

Learning to Align: Addressing Character Frequency Distribution Shifts in Handwritten Text Recognition

Do moral values align between images and the fictional stories about them, within human narratives and between humans and large language models (LLMs)? This question matters because stories are central to how humans communicate moral values, yet little is known about how people and LLMs perform this task in a multimodal (text and image) setting. We present a systematic comparison of moral values represented in human- and LLM-generated narratives based on images annotated by humans for moral content. Our analysis shows that while human stories reflect a balanced distribution of moral foundations and coherent narrative arcs, LLMs disproportionately emphasize the Care foundation and often lack emotional resolution. Even with moral conditioning, these biases persist. We introduce a novel dataset and framework for evaluating moral storytelling in vision-language models, highlighting key challenges in aligning AI with human moral reasoning across cultures.

Tales of Morality: Comparing Human- and LLM-Generated Moral Stories from Visual Cues

Large vision-language models (VLMs) often struggle to generate long and factual captions. However, traditional measures for hallucination and factuality are not well suited for evaluating longer, more diverse captions and in settings where ground-truth human-annotated captions are unavailable. We introduce OVFact, a novel method for measuring caption factuality of long captions that leverages open-vocabulary visual grounding and tool-based verification without depending on human annotations. Our method improves agreement with human judgements and captures both caption descriptiveness (recall) and factual precision in the same metric. Furthermore, unlike previous metrics, our reference-free method design enables new applications towards factuality-based data filtering. We observe models trained on an OVFact-filtered (2.5-5x less) subset of a large-scale, noisy (VLM-generated) pretraining set meaningfully improve factuality precision without sacrificing caption descriptiveness across a range of downstream long caption benchmarks.

OVFact: Measuring and Improving Open-Vocabulary Factuality for Long Caption Models

With the growing adoption of Large Language Models (LLMs) for open-ended tasks, accurately assessing epistemic uncertainty, which reflects a model’s lack of knowledge, has become crucial to ensuring reliable outcomes. However, quantifying epistemic uncertainty in such tasks is challenging due to the presence of aleatoric uncertainty, which arises from multiple valid answers. While bias can introduce noise into epistemic uncertainty estimation, it may also reduce noise from aleatoric uncertainty. To investigate this trade-off, we conduct experiments on Visual Question Answering (VQA) tasks and find that mitigating prompt-introduced bias improves uncertainty quantification in GPT-4o. Building on prior work showing that LLMs tend to copy input information when model confidence is low, we further analyze how these prompt biases affect measured epistemic and aleatoric uncertainty across varying bias-free confidence levels with GPT-4o and Qwen2-VL. We find that all considered biases induce greater changes in both uncertainties when bias-free model confidence is lower. Moreover, lower bias-free model confidence leads to greater underestimation of epistemic uncertainty (i.e. overconfidence) due to bias, whereas it has no significant effect on the direction of changes in aleatoric uncertainty estimation. These distinct effects deepen our understanding of bias mitigation for uncertainty quantification and potentially inform the development of more advanced techniques.

The Role of Model Confidence on Bias Effects in Measured Uncertainties for Vision-Language Models

Recent works began to automate the design of agentic systems using meta-agents that propose and iteratively refine new agent architectures. In this paper, we examine three key challenges in a common class of meta-agents. \emph{First}, we investigate how a meta-agent learns across iterations and find that simply expanding the context with all previous agents, as proposed by previous works, performs worse than ignoring prior designs entirely. We show that the performance improves with an evolutionary approach. \emph{Second}, although the meta-agent designs multiple agents during training, it typically commits to a single agent at test time. We find that the designed agents have low behavioral diversity, limiting the potential for their complementary use. \emph{Third}, we assess when automated design is economically viable. We find that only in a few cases—specifically, two datasets—the overall cost of designing and deploying the agents is lower than that of human-designed agents when deployed on over 15,000 examples. In contrast, the performance gains for other datasets do not justify the design cost, regardless of scale.

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Next from EMNLP 2025

Fine-Tuning Encoder-Decoder Models with Contrastive Learning for In-Context Distractor Generation

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