Austria

Reliable multilingual evaluation is difficult, and culturally appropriate evaluation is even harder to achieve.
A common practice to fill this gap is to machine-translate English evaluation sets. However, translation introduces language bias and carries over cultural and regional assumptions from the original questions -- often testing knowledge irrelevant to the target audience. 
In this work, we highlight the extent and impact of these biases and present a multilingual evaluation framework that aims to mitigate them through improved translations and annotation practices.
Through a large-scale study involving professional and community translators and annotators, we show that state-of-the-art models excel primarily by learning Western-centric concepts. Notably, we find that model rankings on the full MMLU change when evaluated on a subset of questions explicitly marked as culturally sensitive.
We release Global MMLU, a multilingual extension of MMLU across 42 languages, featuring improved translation quality, expanded language coverage, and designated subsets labeled as culturally sensitive and culturally agnostic to enable a more comprehensive and equitable benchmark for evaluating language models across diverse linguistic and cultural contexts.

ACL 2025

Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation

mmlu

multilingual evaluation

cultural bias

## 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*

EMNLP 2025

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

poster

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Message from the General Chair: 
*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.* 
[Read more](https://drive.google.com/file/d/1GI_hvOpjswAuYdUTromfeDiPpCcqidwg/view?usp=sharing)

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Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Social psychologists have shown that Warmth (W) and Competence (C) are the primary dimensions along which we assess other people and groups. These dimensions impact various aspects of our lives from social competence and emotion regulation to success in the work place and how we view the world. More recent work has started to explore how these dimensions develop, why they have developed, and what they constitute. Of particular note, is the finding that warmth has two distinct components: Trust (T) and Sociability (S). In this work, we introduce Words of Warmth, the first large-scale repository of manually derived word--warmth (as well as word--trust and word--sociability) associations for over 26k English words. We show that the associations are highly reliable. We use the lexicons to study the rate at which children acquire WCTS words with age. Finally, we show that the lexicon enables a wide variety of bias and stereotype research through case studies on various target entities. Words of Warmth is freely available at: http://saifmohammad.com/warmth.html

Words of Warmth: Trust and Sociability Norms for over 26k English Words

The reward model has become increasingly important in alignment, assessment, and data construction for large language models (LLMs). Most existing researchers focus on enhancing reward models through data improvements, following the conventional training framework for reward models that directly optimizes the predicted rewards.
In this paper, we propose a hybrid alignment framework **HAF-RM** for reward model training by introducing an additional constraint on token-level policy probabilities in addition to the reward score. 
It can simultaneously supervise the internal preference model at the token level and optimize the mapping layer of the reward model at the sequence level.
Experiment results on five datasets sufficiently show the validity and effectiveness of our proposed hybrid framework for training a high-quality reward model.
By decoupling the reward modeling procedure and incorporating hybrid supervision, our **HAF-RM** framework offers a principled and effective approach to enhancing the performance and alignment of reward models, a critical component in the responsible development of powerful language models. We release our code at [https://haf-rm.github.io](https://haf-rm.github.io).

HAF-RM: A Hybrid Alignment Framework for Reward Model Training

Current large language models (LLMs) often exhibit imbalanced multilingual capabilities due to their English-centric training corpora. To address this, existing fine-tuning approaches operating at the data-level (e.g., through data augmentation or distillation) typically introduce implicit cross-lingual alignment, overlooking the potential for more profound, latent-level cross-lingual interactions. In this work, we propose CC-Tuning, a novel multilingual fine-tuning paradigm that explicitly establishes a cross-lingual connection mechanism at the latent level. During training, CC-Tuning fuses the feed forward activations from both English and non-English inputs, enabling the model to benefit from both linguistic resources. This process is facilitated with a trainable Decision Maker that identifies beneficial activations. Furthermore, during inference, a Transform Matrix is utilized to simulate the cross-lingual connection under monolingual setting through representation transformation. Our experiments on six benchmarks covering 22 languages show that CC-Tuning outperforms vanilla SFT and offers a strong latent-level alternative to data-level augmentation methods. Further analysis also highlights the practicality of CC-Tuning and the potential of latent-level cross-lingual interactions in advancing the multilingual performance of LLMs.

CC-Tuning: A Cross-Lingual Connection Mechanism for Improving Joint Multilingual Supervised Fine-Tuning

Synthetic data has driven recent state-of-the-art advancements, but reliance on a single oracle teacher model can lead to model collapse and bias propagation. These issues are particularly severe in multilingual settings, where no single model excels across all languages. In this study, we propose multilingual arbitration, which exploits performance variations among multiple models for each language. By strategically routing samples through a diverse set of models, each with unique strengths, we mitigate these challenges and enhance multilingual performance. Extensive experiments with state-of-the-art models demonstrate that our approach significantly surpasses single-teacher distillation, achieving up to 80% win rates over proprietary and open-weight models like Gemma 2, Llama 3.1, and Mistral v0.3, with the largest improvements in low-resource languages.

Multilingual Arbitration: Optimizing Data Pools to Accelerate Multilingual Progress

Existing LLM-based agents have achieved strong performance on held-in tasks, but their generalizability to unseen tasks remains poor. Hence, some recent work focus on fine-tuning the policy model with more diverse tasks to improve the generalizability. In this work, 
we find that finetuning a reward model to guide the policy model is more robust than directly finetuning the policy model.
Based on this finding, we propose AgentRM, a 8B generalizable reward model, to guide the policy model for effective test-time search.
We comprehensively investigate three approaches to construct the reward model, including explicit reward modeling, implicit reward modeling and LLM-as-a-judge.
We then use AgentRM to guide the answer generation with Best-of-N sampling and beam search.
We show that AgentRM is robust to paraphrasings of task instructions and can generalize to unseen tasks that require novel optimal behavior.
Through extensive evaluation across nine tasks spanning four categories, AgentRM enhances the non-finetuned 8B policy model by 8.8 points on average, surpassing the top general agent by 4.0.
Moreover, it demonstrates weak-to-strong generalization, yielding greater improvement on more powerful policy models.
As for the specializability, AgentRM can also boost a finetuned policy model and outperform the top specialized agent by 11.4 on three held-in tasks.
Further analysis verifies its effectiveness in test-time scaling.
We release the code and data at https://github.com/thunlp/AgentRM.

AgentRM: Enhancing Agent Generalization with Reward Modeling

Causal masking is a fundamental component in Generative Pre-Trained Transformer (GPT) models, playing a crucial role during training. Although GPTs can process the entire user prompt at once, the causal masking is applied to all input tokens step-by-step, mimicking the generation process. This imposes an unnecessary constraint during the initial "prefill" phase when the model processes the input prompt and generates the internal representations before producing any output tokens. In this work, attention is masked based on the known block structure at the prefill phase, followed by the conventional token-by-token autoregressive process after that. For example, in a typical chat prompt, the system prompt is treated as one block, and the user prompt as the next one. Each of these is treated as a unit for the purpose of masking, such that the first tokens in each block can access the subsequent tokens in a non-causal manner. Then, the model answer is generated in the conventional causal manner. The Segment-by-Segment scheme entails no additional computational overhead. When integrated using a lightweight fine-tuning into already trained models such as Llama and Qwen, MAS quickly increases models' performances.

Segment-Based Attention Masking for GPTs

High computation costs and latency of large language models such as GPT-4 have limited their deployment in clinical settings. Small language models (SLMs) offer a cost-effective alternative, but their limited capacity requires biomedical domain adaptation, which remains challenging. An additional bottleneck is the unavailability and high sensitivity of clinical data. To address these challenges, we propose a novel framework for adapting SLMs into high-performing clinical models. We introduce the MediPhi collection of 3.8B-parameter SLMs developed with our novel framework: pre-instruction tuning of experts on relevant medical and clinical corpora (PMC, Medical Guideline, MedWiki, etc.), model merging, and clinical-tasks alignment. To cover most clinical tasks, we extended the CLUE benchmark to CLUE+, doubling its size. Our expert models deliver relative improvements on this benchmark over the base model without any task-specific fine-tuning: 64.3% on medical entities, 49.5% on radiology reports, and 44% on ICD-10 coding (outperforming GPT-4-0125 by 14%). We unify the expert models into MediPhi via model merging, preserving gains across benchmarks. Furthermore, we built the MediFlow collection, a synthetic dataset of 2.5 million high-quality instructions on 14 medical NLP tasks, 98 fine-grained document types, and JSON format support. Alignment of MediPhi using supervised fine-tuning and direct preference optimization achieves further gains of 18.9% on average.

A Modular Approach for Clinical SLMs Driven by Synthetic Data with Pre-Instruction Tuning, Model Merging, and Clinical-Tasks Alignment

Incorporating automatic speech recognition (ASR) into field linguistics workflows for language documentation has become increasingly common. While ASR performance has seen improvements in low-resource settings, obstacles remain when training models on data collected by documentary linguists. One notable challenge lies in the way that this data is curated. ASR datasets built from spontaneous speech are typically recorded in consistent settings and transcribed by native speakers following a set of well designed guidelines. In contrast, field linguists collect data in whatever format it is delivered by their language consultants and transcribe it as best they can given their language skills and the quality of the recording. This approach to data curation, while valuable for linguistic research, does not always align with the standards required for training robust ASR models. In this paper, we explore methods for identifying speech transcriptions in fieldwork data that may be unsuitable for training ASR models. We focus on two complimentary automated measures of transcription quality that can be used to identify transcripts with characteristics that are common in field data but could be detrimental to ASR training. We show that one of the metrics is highly effective at retrieving these types of transcriptions. Additionally, we find that filtering datasets using this metric of transcription quality reduces WER both in controlled experiments using simulated fieldwork with artificially corrupted data and in real fieldwork corpora.

That doesn’t sound right: Evaluating speech transcription quality in field linguistics corpora

Scaling the test-time compute of large language models has demonstrated impressive performance on reasoning benchmarks. However, existing evaluations of test-time scaling make the strong assumption that a reasoning system should always give an answer to any question provided. This overlooks concerns about whether a model is confident in its answer, and whether it is appropriate to always provide a response. To address these concerns, we extract confidence scores during reasoning for thresholding model responses. We find that increasing compute budget at inference time not only helps models answer more questions correctly, but also increases confidence in correct responses. We then extend the current paradigm of zero-risk responses during evaluation by considering settings with non-zero levels of response risk, and suggest a recipe for reporting evaluations under these settings.

Is That Your Final Answer? Test-Time Scaling Improves Selective Question Answering

Multimodal learning has celebrated remarkable success across diverse areas, yet faces the challenge of prohibitively expensive data collection and annotation when adapting models to new environments. In this context, domain adaptation has gained growing popularity as a technique for knowledge transfer, which, however, remains underexplored in multimodal settings compared with unimodal ones. This paper investigates multimodal domain adaptation, focusing on a practical partially shifting scenario where some modalities (referred to as anchors) remain domain-stable, while others (referred to as drifts) undergo a domain shift. We propose a bi-alignment scheme to simultaneously perform drift-drift and anchor-drift matching. The former is achieved through adversarial learning, aligning the representations of the drifts across source and target domains; the latter corresponds to an "anchor dragging drift" strategy, which matches the distributions of the drifts and anchors within the target domain using the optimal transport (OT) method. The overall design principle features \textbf{A}dversarial \textbf{A}lignment with \textbf{A}nchor \textbf{D}ragging \textbf{D}rift, abbreviated as \textbf{$A^3D^2$}, for multimodal domain adaptation with partially shifted modalities. Comprehensive empirical results verify the effectiveness of the proposed approach, and demonstrate that $A^3D^2$ achieves superior performance compared with state-of-the-art approaches. The code is available at: \url{https://github.com/sunjunaimer/A3D2.git}.

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Words of Warmth: Trust and Sociability Norms for over 26k English Words

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