China

Once language models (LMs) are deployed, they can interact with users long-term, ideally evolving continuously based on user feedback. Asking direct feedback from users can be costly and disruptive, motivating our research in harvesting implicit user feedback from user interaction logs. In this work, we study implicit user feedback in two user-LM interaction datasets (WildChat and LMSYS). First, we analyze user feedback in the human-LLM conversation trajectory, providing insights on the patterns of implicit user feedback. Second, we study harvesting learning signals from such implicit user feedback. We find that the contents of user feedback (e.g., user wanted clarification), not just the polarity (e.g., users were unhappy with the previous model response), can provide helpful signals for improving model performance in some settings but not universally. We also find that the usefulness of user feedback is largely tied to the quality of the user&#39;s initial prompt. Together, we provide an in-depth study in implicit user feedback, showing its potential and limitations.

EMNLP 2025

User Feedback in Human-LLM Dialogues: A Lens to Understand Users But Noisy as a Learning Signal

human-llm interactions

human feedback

alignment

The 8th AAAI Conference on Human Computation and Crowdsourcing (HCOMP 2020) will be held October 26–28th virtually at The Netherlands Institute for Sound and Vision in Hilversum, The Netherlands.

While artificial intelligence (AI) and human-computer interaction (HCI) represent traditional mainstays of the conference, HCOMP believes strongly in fostering and promoting broad, interdisciplinary research. Our field is particularly unique in the diversity of disciplines it draws upon and contributes to, including human-centered qualitative studies and HCI design, social computing, artificial intelligence, economics, computational social science, digital humanities, policy, and ethics. We promote the exchange of advances in human computation and crowdsourcing not only among researchers, but also engineers and practitioners, to encourage dialogue across disciplines and communities of practice.

This year, we especially invite works that will generate new insights into the quality (or qualities) of human-annotated datasets, including elements such as reliability and replicability of human computation and crowdsourcing experiments, novel metrics for aggregation of results, and holistic approaches to deal with bias, fairness and interpretability.

HCOMP 2020

HCOMP believes strongly in fostering and promoting broad, interdisciplinary research. Our field is particularly unique in the diversity of disciplines it draws upon and contributes to, including human-centered qualitative studies and HCI design, social computing, artificial intelligence, economics, computational social science, digital humanities, policy, and ethics. 


Once language models (LMs) are deployed, they can interact with users long-term, ideally evolving continuously based on user feedback. Asking direct feedback from users can be costly and disruptive, motivating our research in harvesting implicit user feedback from user interaction logs. In this work, we study implicit user feedback in two user-LM interaction datasets (WildChat and LMSYS). First, we analyze user feedback in the human-LLM conversation trajectory, providing insights on the patterns of implicit user feedback. Second, we study harvesting learning signals from such implicit user feedback. We find that the contents of user feedback (e.g., user wanted clarification), not just the polarity (e.g., users were unhappy with the previous model response), can provide helpful signals for improving model performance in some settings but not universally. We also find that the usefulness of user feedback is largely tied to the quality of the user's initial prompt. Together, we provide an in-depth study in implicit user feedback, showing its potential and limitations.

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.

Compositional reasoning remains a persistent weakness of modern vision language models (VLMs): they often falter when a task hinges on understanding how multiple objects, attributes, and relations interact within an image. Multiple research works have attempted to improve compositionality performance by creative tricks such as improving prompt structure, chain of thought reasoning, etc. A more recent line of work attempts to impart additional reasoning in VLMs using well-trained Large Language Models (LLMs), which are far superior in linguistic understanding than VLMs to compensate for the limited linguistic prowess of VLMs. However, these approaches are either resource-intensive or do not provide an interpretable reasoning process. In this paper, we present "COCO-Tree" - a novel approach that augments VLM outputs with carefully designed neurosymbolic concept trees learned from LLMs to improve VLM's linguistic reasoning. COCO-Tree's beam search-inspired reasoning process boosts compositionality performance and provides a rationale behind VLM predictions. Empirical results on four compositionality benchmarks, Winoground, EqBench, ColorSwap, and SugarCrepe, in seven different open-source VLMs with varying sizes, demonstrate that COCO-Tree significantly improves compositional generalization by 5-10% over baselines.

COCO-Tree: Compositional Hierarchical Concept Trees for Enhanced Reasoning in Vision-Language Models

Autoregressive generative models play a key role in various language tasks, especially for modeling and evaluating long text sequences. While recent methods leverage stochastic representations to better capture sequence dynamics, encoding both temporal and structural dependencies and utilizing such information for evaluation remains challenging. In this work, we observe that fitting transformer-based model embeddings into a stochastic process yields ordered latent representations from originally unordered model outputs. Building on this insight and prior work, we theoretically introduce a novel likelihood-based evaluation metric BBScoreV2. Empirically, we demonstrate that the stochastic latent space induces a "clustered-to-temporal ordered" mapping of language model representations in high-dimensional space, offering both intuitive and quantitative support for the effectiveness of BBScoreV2. Furthermore, this structure aligns with intrinsic properties of natural language and enhances performance on tasks such as temporal consistency evaluation (e.g., Shuffle tasks) and AI-generated content detection.

BBScoreV2: Learning Time-Evolution and Latent Alignment from Stochastic Representation

We introduce Paralinguistic Speech Captions (ParaSpeechCaps), a large-scale dataset that annotates speech utterances with rich style captions. While rich abstract tags (e.g. guttural, nasal, pained) have been explored in small-scale human-annotated datasets, existing large-scale datasets only cover basic tags (e.g. low-pitched, slow, loud). We combine off-the-shelf text and speech embedders, classifiers and an audio language model to automatically scale rich tag annotations for the first time. ParaSpeechCaps covers a total of 59 style tags, including both speaker-level intrinsic tags and utterance-level situational tags. It consists of 282 hours of human-labelled data (PSC-Base) and 2450 hours of automatically annotated data (PSC-Scaled). We finetune Parler-TTS, an open-source style-prompted TTS model, on ParaSpeechCaps, and achieve improved style consistency (+7.9% Consistency MOS) and speech quality (+15.5% Naturalness MOS) over the best performing baseline that combines existing rich style tag datasets. We ablate several of our dataset design choices to lay the foundation for future work in this space. ParaSpeechCaps and our trained models will be open-sourced.

Scaling Rich Style-Prompted Text-to-Speech Datasets

Group Relative Policy Optimization (GRPO), which is widely adopted by R1-like reasoning models, has advanced mathematical reasoning. Nevertheless, GRPO faces challenges in reward sparsity, verbosity, and inadequate focus on problem difficulty. We propose GRPO-LEAD, enhancing GRPO with: (1) length-regularized rewards to encourage conciseness while maintaining accuracy; (2) explicit penalties for incorrect solutions to improve model precision; and (3) difficulty-aware advantage reweighting for robust generalization on challenging problems. Comprehensive evaluations demonstrate that GRPO-LEAD significantly improves reasoning accuracy, conciseness, and efficiency. Our approach achieves state-of-the-art performance for 14B-scale models, underscoring the synergy of our methods with appropriate model scale and high-quality data. Our source code, generated dataset, and models are available after the acceptance of this paper.

GRPO-LEAD: A Difficulty-Aware Reinforcement Learning Approach for Concise Mathematical Reasoning in Language Models

In this study, we investigate how different text types affect eye movements during reading, how decoding strategies used to generate texts interact with text type, and how text types modulate the influence of word-level psycholinguistic features such as surprisal, word length, and lexical frequency. Leveraging EMTeC (Bolliger et al., 2025), the first eye-tracking corpus of LLM-generated texts across six text types and multiple decoding algorithms, we show that text type strongly modulates cognitive effort during reading, that psycholinguistic effects induced by word-level features vary systematically across genres, and that decoding strategies interact with text types to shape reading behavior. These findings offer insights into genre-specific cognitive processing and have implications for the human-centric design of AI-generated texts.

Genre Matters: How Text Types Interact with Decoding Strategies and Lexical Predictors in Shaping Reading Behavior

Although region-specific large language models (LLMs) are increasingly developed, their safety remains underexplored, particularly in culturally diverse settings like Indonesia, where sensitivity to local norms is essential and highly valued by the community. In this work, we present IndoSafety, the first high-quality, human-verified safety evaluation dataset tailored for the Indonesian context, covering five language varieties: formal and colloquial Indonesian, along with three major local languages: Javanese, Sundanese, and Minangkabau. IndoSafety is constructed by extending prior safety frameworks to develop a taxonomy that captures Indonesia’s sociocultural context. We find that existing Indonesian-centric LLMs often generate unsafe outputs, particularly in colloquial and local language settings, while fine-tuning on IndoSafety significantly improves safety while preserving task performance. Our work highlights the critical need for culturally grounded safety evaluation and provides a concrete step toward responsible LLM deployment in multilingual settings. Warning: This paper contains example data that may be offensive, harmful, or biased.

IndoSafety: Culturally Grounded Safety for LLMs in Indonesian Languages

Homophone normalization--where characters that have the same sound in a writing script are mapped to one character--is a pre-processing step applied in Amharic Natural Language Processing (NLP) literature. While this may improve performance reported by automatic metrics, it also results in models that are not able to understand different forms of writing in a single language. Further, there might be impacts in transfer learning, where models trained on normalized data do not generalize well to other languages. In this paper, we experiment with monolingual training and cross-lingual transfer to understand the impacts of normalization on languages that use the Ge'ez script. We then propose a post-inference intervention in which normalization is applied to model predictions instead of training data. With our simple scheme of post-inference normalization, we show that we can achieve an increase in BLEU score of up to 1.03 while preserving language features in training.

A Case Against Implicit Standards: Homophone Normalization in Machine Translation for Languages that use the Ge’ez Script.

Machine Translation errors in high-stakes settings like healthcare pose unique risks that could lead to clinical harm. The challenges are even more pronounced for low-resourced languages where human translators are scarce and MT tools perform poorly. In this work, we provide a taxonomy of Machine Translation errors for the healthcare domain using a publicly available MT system. Preparing an evaluation dataset from pre-existing medical datasets, we conduct our study focusing on two low-resourced languages: Amharic and Tigrinya. Based on our error analysis and findings from prior work, we test two pre-translation interventions--namely, paraphrasing the source sentence and pivoting with a related language-- for their effectiveness in reducing clinical risk. We find that MT errors for healthcare most commonly happen when the source sentence includes medical terminology and procedure descriptions, synonyms, figurative language, and word order differences. We find that pre-translation interventions are not effective in reducing clinical risk if the base translation model performs poorly. Based on our findings, we provide recommendations for improving MT for healthcare.

Viability of Machine Translation for Healthcare in Low-Resourced Languages

Aligning large language models (LLMs) with human preferences has become a critical step in their development. Recent research has increasingly focused on test-time alignment, where additional compute is allocated during inference to enhance LLM safety and reasoning capabilities. However, these test-time alignment techniques often incur substantial inference costs, limiting their practical application. We are inspired by the speculative sampling acceleration, which leverages a small draft model to efficiently predict future tokens, to address the efficiency bottleneck of test-time alignment. We introduce the reward-shifted speculative sampling (SSS) algorithm, in which the draft model is aligned with human preferences, while the target model remains unchanged. We theoretically demonstrate that the distributional shift between the aligned draft model and the unaligned target model can be exploited to recover the RLHF optimal solution without actually obtaining it, by modifying the acceptance criterion and bonus token distribution. Our algorithm achieves superior gold reward scores at a significantly reduced inference cost in test-time weak-to-strong alignment experiments, thereby validating both its effectiveness and efficiency.

Reward-Shifted Speculative Sampling Is An Efficient Test-Time Weak-to-Strong Aligner

Drug discovery is a long, expensive, and complex process, relying heavily on human medicinal chemists, who can spend years searching the vast space of potential therapies. Recent advances in artificial intelligence for chemistry have sought to expedite individual drug discovery tasks; however, there remains a critical need for an intelligent agent that can navigate the drug discovery process. Towards this end, we introduce LIDDiA, an autonomous agent capable of intelligently navigating the drug discovery process in silico. By leveraging the reasoning capabilities of large language models, LIDDiA serves as a low-cost and highly-adaptable tool for autonomous drug discovery. We comprehensively examine LIDDiA, demonstrating that (1) it can generate molecules meeting key pharmaceutical criteria on over 70% of 30 clinically relevant targets, (2) it intelligently balances exploration and exploitation in the chemical space, and (3) it can identify promising novel drug candidates on EGFR, a critical target for cancers. Code and dataset are available at https://urlvanish.com/e18d684c.

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Next from EMNLP 2025

COCO-Tree: Compositional Hierarchical Concept Trees for Enhanced Reasoning in Vision-Language Models

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