United States

ACL 2026

Reference Games as a Testbed for the Alignment of Model Uncertainty and Clarification Requests

workshop paper

It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields 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.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. <br>
[Full Message from General Chair Philipp Koehn](https://drive.google.com/file/d/1QiJFOmMjvDgsDo3i1XzlQnImxe3RU_Cy/view?usp=sharing) <br><br><br>
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The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

Automatic Paper Analysis and Categorisation for Systematic Reviews with Combined Reasoning-Augmented SFT and DAPO RL

LLM Multi-Agent Systems for Long Triple Set Data-to-Text Generation

MedScore: Generalizable Factuality Evaluation of Open-ended Long-form Medical Answers by Domain-adapted Claim Decomposition and Verification

ReproNLP 2026: A Third Replication of the Human Evaluation of a QAG System for Children’s Storybooks

Do Nugget-Based Evaluation Patterns Generalize to List-QA?

Carefully Considering Culture: Analyzing LLM Alignment in Single- and Multi-Cultural Settings using Cultural Consensus Theory

Bangla memes are widely used on social me dia to express humor and social commentary, yet computational analysis of gender bias in Bangla memes remains largely unexplored. In this work, we present a multimodal framework for detecting gender bias in Bangla memes by jointly analyzing textual and visual con tent. We construct a dataset of 6,846 Bangla and Banglish code-mixed memes annotated into three categories: male-biased, female biased, and neutral. For textual representa tion, we use BanglishBERT, while visual fea tures are extracted using ConvNeXt, and the two modalities are fused for final classifica tion. Our best-performing model, ConvNeXt + BanglishBERT, achieves accuracy of 0.67 and an F1-score of 0.63, outperforming sev eral multimodal baselines. The results demon strate the effectiveness of multimodal learn ing for understanding culturally nuanced and code-mixed meme content in low-resource lan guages.

Behind the Laughter: Uncovering Gender Bias in Code-Mixed Bangla Memes

Bias and fairness risks in Large Language Mod els (LLMs) vary substantially across deploy ment contexts, yet existing approaches lack systematic guidance for selecting appropriate evaluation metrics. We present a decision framework that maps LLM use cases, charac terized by a model and population of prompts, to relevant bias and fairness metrics based on task type, whether prompts contain protected attribute mentions, and stakeholder priorities. Our framework addresses toxicity, stereotyp ing, counterfactual unfairness, and allocational harms, and introduces novel metrics based on stereotype classifiers and counterfactual adapta tions of text similarity measures. We release an open-source Python library, langfair, for practical adoption. Extensive experiments on use cases across five LLMs and five prompt populations demonstrate that fairness risks can not be reliably assessed from benchmark per formance alone: results on one prompt dataset likely overstate or understate risks for another, underscoring that fairness evaluation must be grounded in the specific deployment context.

Bring Your Own Prompts: Use-Case-Specific Bias and Fairness Evaluation for LLMs

Multilingual Natural Language Understanding (NLU) systems often struggle to adapt when new languages or new semantic labels are in troduced with only a few annotated examples. This challenge is particularly pronounced for low-resource languages, where limited super vision and evolving label spaces make conven tional joint-label classification approaches un stable. Most existing multilingual NLU models treat each language–semantic pair as an inde pendent class, entangling linguistic and seman tic representations and hindering few-shot adap tation. We propose Dual-Axis Compositional Few-Shot Learning, a framework that explicitly factorizes the representation space into linguis tic and semantic embedding axes, enabling in dependent modeling of language variation and domain–intent semantics. Joint representations are constructed compositionally through mul tiplicative interaction of axis-specific embed dings, allowing controlled adaptation when ei ther the language set or the semantic label space evolves. The framework integrates factorized prototype learning, axis-structured contrastive alignment, and disentanglement regularization using HSIC-based statistical independence and Jacobian-based cross-axis decorrelation. Ex periments on six low-resource Indic languages spanning Indo-Aryan and Dravidian families (Hindi, Bengali, Sanskrit, Assamese, Tamil, and Telugu) demonstrate strong performance under two structured generalization regimes. The model achieves 81.12% accuracy when adapting to few-shot languages with known semantics and 63.5% accuracy when learning new semantic classes from few-shot examples, along with an accuracy of 89.56% on known languages and seen semantics. These results show that axis-factorized representations en able stable compositional generalization, offer ing a promising direction for scalable multilin gual NLU in linguistically diverse low-resource settings.

Dual-Axis Compositional Contrastive Few-Shot Learning using Prototypes Across Linguistic and Semantic Dimensions for Indic Low-Resource Multilingual NLU

Automatic text simplification (ATS) aims to en hance language accessibility for various target groups, particularly persons with intellectual disabilities. Recent advancements in large lan guage models (LLMs) have substantially im proved the quality of machine-generated text simplifications, however, existing LLM-based ATS systems do not incorporate preference feedback during post-training, resulting in a lack of personalization tailored to the specific needs of target group persons. In this work, we propose an ATS personalization framework using direct preference optimization (DPO). Specifically, we post-trained LLM-based ATS models using human feedback collected from persons with intellectual disabilities, reflecting their preferences of paired text simplifications generated by mainstream LLMs. Our pipeline for developing personalized LLM-based ATS systems encompasses data collection, model se lection, supervised fine-tuning (SFT) and DPO post-training, and result evaluation. Our find ings underscore the necessity of active partici pation of target group persons in designing per sonalized inclusive AI solutions aligned with human preferences.

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Automatic Paper Analysis and Categorisation for Systematic Reviews with Combined Reasoning-Augmented SFT and DAPO RL

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Next from ACL 2026

Automatic Paper Analysis and Categorisation for Systematic Reviews with Combined Reasoning-Augmented SFT and DAPO RL

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

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