Morocco

Domain-specific Named Entity Recognition (NER) often requires data augmentation due to the scarcity of annotated corpora. Guidance Data Augmentation (GDA), a method utilizing Large Language Models (LLMs) to decompose sentences into abstract components, can lead to over-abstraction, resulting in undefined entity tags and sentences lacking domain-specific vocabulary. In this work, we propose Reflective GDA (R-GDA), a framework that introduces a multi-agent feedback loop to enhance augmentation quality. R-GDA incorporates two distinct agents: a **Guidance Refiner (GR)**, which assesses the initial abstraction to prevent over-generalization, and an **Augmentation Calibrator (AC)**, which validates the final generated sample for domain-fidelity and tag integrity. On the SciERC and NCBI-disease datasets, R-GDA improves F1-Score, validating its effectiveness. Concurrently, it achieves low BERTScore in most cases, indicating greater sentence diversity. For the FIN dataset, it achieves performance comparable to the GDA baseline. R-GDA consistently prevents errors regarding domain-specific tags, demonstrating that the reflective feedback mechanism enhances data fidelity by mitigating critical generation errors.

EACL 2026

R-GDA: Reflective Guidance Data Augmentation with Multi-Agent Feedback for Domain-Specific Named Entity Recognition

multi-agent feedback

named entity recognition (ner)

data augmentation

information extraction

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.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.<br><br>

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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.

technical paper

#### *Message from the General Chair, Aline Villavicencio*
I’m delighted and honoured to welcome you to the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2026), taking place in the beautiful city of Rabat, in Morocco, in March 24-29, 2026. EACL is the flagship European conference of the Association and EACL 2026 proudly continues our field’s tradition of excellence in scholarship, innovation, and inclusivity. I am deeply grateful to the many volunteers whose dedication, generosity, and tireless efforts have made this conference possible.
For the first time EACL is being hosted in the African continent. This is an important milestone for our community, and we are grateful to our Moroccan hosts for enabling this historic moment by bringing this edition of EACL to Rabat. We are also delighted that the Second Arabic NLP School is co-located with EACL. We hope attendees enjoy this wonderful opportunity to strengthen ties with the Computational Linguistics communities across the African continent. *[Read full message](https://drive.google.com/file/d/14NlmHvwM6fPJuMmOvVh7K0vtQbEyv3SZ/view?usp=sharing)*<br><br>

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Welcome to the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL). EACL 2026 will be held in Rabat, Morocco, from March 24–29, 2026. 

Historically, LLMs have been trained using either autoregressive (AR) or masked language modeling (MLM) objectives, with AR models gaining dominance in recent years. However, AR models are inherently incapable of masked infilling, which is the ability to predict masked tokens between past and future context. In contrast, MLM models suffer from intrinsic computational inefficiencies during both training and inference that hinder their scalability. This work introduces MARIA (Masked and Autoregressive Infilling Architecture), a novel approach that leverages the strengths of both paradigms to achieve state-of-the-art masked infilling performance. MARIA combines a pre-trained MLM and AR model by training a linear decoder that takes their concatenated hidden states as input. This minimal modification enables the AR model to perform infilling while retaining its inherent advantages in terms of faster inference with KV caching. Our results demonstrate that MARIA significantly outperforms existing methods, namely discrete diffusion models, on masked infilling tasks.

Enabling Autoregressive Models to Fill In Masked Tokens

Safety policies are critical for content moderation of online communities, but crafting them remains costly and labor-intensive. We present Deep Policy Research (DPR), an agentic framework that leverages large language models (LLMs) and web search to automatically construct domain-specific safety policies. Given only a high-level domain specification, DPR iteratively proposes subtopics, performs open-domain search, and distills retrieved information into structured policy rules. We evaluate DPR across two domains: (1) online content moderation using the OpenAI content moderation dataset and (2) multimodal advertisement moderation with an in-house benchmark dataset. Our results show that DPR-generated policies consistently outperform simple baselines and are on par with human written policies in terms of effectiveness, suggesting that LLM-based open-domain research offers a promising avenue for bootstrapping safety policies across diverse applications.

Open-Domain Safety Policy Construction

Designing user-centered LLM systems requires understanding how people use them, but patterns of user behavior are often masked by the variability of queries. In this work, we introduce a new framework to describe request-making that segments user input into request content, roles assigned, query-specific context, and the remaining task-independent expressions. We apply the workflow to create and analyze a dataset of 211k real-world queries based on WildChat. Compared with similar human-human setups, we find significant differences in the language for request-making in the human-LLM scenario. Further, we introduce a novel and essential perspective of diachronic analyses with user expressions, which reveals fundamental and habitual user-LLM interaction patterns beyond individual task completion. We find that query patterns evolve from early ones emphasizing sole requests to combining more context later on, and individual users explore expression patterns but tend to converge with more experience. From there, we propose to understand communal trends of expressions underlying distinct tasks and discuss the preliminary findings. Finally, we discuss the key implications for user studies, computational pragmatics, and LLM alignment.

Show or Tell? Modeling the evolution of request-making in Human-LLM conversations

Large vision-language models (LVLMs) are gaining traction in clinical tasks such as diagnostic support, report generation, and medical question answering. Among post-training techniques, Direct Preference Optimization (DPO) has shown promise in aligning model outputs with human preferences, yet its effectiveness in high-stakes medical contexts remains underexplored. In this work, we present the first systematic evaluation of nine DPO variants applied to two leading medical LVLMs, LLaVA-Med and HuatuoGPT-Vision. We benchmark these models on five curated datasets covering diverse clinical tasks. Evaluations include both automated metrics and expert assessments. Our results show that while DPO improves alignment and reduces severe hallucinations, it yields inconsistent gains over supervised fine-tuning. We further introduce DPO variant that better handles visual misinterpretations and enhances clinical understanding. These findings reveal both the potential and limitations of DPO in medical AI. To support future research, we will release all DPO training data, model checkpoints, and expert annotations upon acceptance.

Benchmarking Direct Preference Optimization for Medical Large Vision-Language Models

High quality summarization data remains scarce in under-represented languages. However, historical newspapers, made available through recent digitization efforts, offer an abundant source of untapped, naturally annotated data. In this work, we present a novel method for collecting naturally occurring summaries via Front-Page Teasers, where editors summarize full length articles. We show that this phenomenon is common across seven diverse languages and supports multi-document summarization. To scale data collection, we develop an automatic process, suited to varying linguistic resource levels. Finally, we apply this process to a Hebrew newspaper title, producing HEBTEASESUM, the first dedicated multi-document summarization dataset in Hebrew.

Leveraging Digitized Newspapers to Collect Summarization Data in Low-Resource Languages

Logical table-to-text generation aims to generate natural language descriptions that fluently and precisely describe the given table with both surface-level and logic-level fidelity. Although large language models (LLMs) have demonstrated strong capabilities in plain text, their proficiency in interpreting and reasoning tabular data is still limited. In this paper, we are the first to comprehensively explore the performance of various LLMs in the logical table-to-text generation task. However, we find that existing LLMs are difficult to achieve satisfactory results in this task. Even worse, existing prompt strategies cannot cope with complex non-chain logical reasoning scenarios on tables. To address the challenges mentioned above, we constructed a new table-related instruction dataset called LogicTableInstruct and instruction-tuned the open-source LLM on this dataset, resulting in the specialized LLM (LogicTableLLaMA-3.1-8B) for table-related tasks. We also introduced a novel reasoning method, Logic Tree-of-Program (LogicToP), to improve the logical reasoning ability of the LLMs on tables. Our extensive experiments on various LLMs demonstrated that LogicToP can effectively improve the performance of LLMs on this task. Our LogicTableLLaMA-3.1-8B model in the 5-shot LogicToP setting achieves state-of-the-art results on the Logic2Text dataset. The code and data will be released to boost future work on table-related tasks.

LogToP: Logic Tree-of-Program with Table Instruction-tuned LLMs for Controlled Logical Table-to-Text Generation

Retrieval-augmented generation (RAG) systems rely on retrieval models for identifying relevant contexts and answer generation models for utilizing those contexts. However, retrievers exhibit imperfect recall and precision, limiting downstream performance. We introduce RAG-RL, an answer generation model trained for multi-hop question answering (MHQA) to not only generate answers but also to identify and cite relevant information from larger sets of retrieved contexts, shifting some of the burden of identifying relevant documents from the retriever to the answer generator. Our approach uses curriculum learning, where models are trained across retrieval settings with varying levels of noise. Our experiments show that training samples with fewer distractor documents enable models to acquire citation and reasoning skills with greater sample efficiency and generalizability, demonstrating strong model performance even as the number of irrelevant passages increases. We benchmark our methods on three open-domain MHQA datasets and report significant gains in answer and citation accuracy. Furthermore, our experiments provide empirical insights into how simpler training samples can give models stronger signals for learning specific skills (e.g., citation generation) and how different components of post-training (e.g., training set construction, rule-based rewards, training sample ordering, etc.) impact final model performance.

Tackling Distractor Documents in Multi-Hop QA with Reinforcement and Curriculum Learning

We propose MaskLoRA, a plug-and-play masking mechanism that turns PEFT's low-rank subspace into a faithful, compute-aware token selector. Instead of relying on attention entropy or gradients, MaskLoRA scores tokens by the LoRA-induced representation shift and learns differentiable gates with an objective combining task loss, expected L 0 sparsity, prediction consistency (KL), and a subspace concentration term. We provide a bound showing the KL between full and masked predictions is controlled by the sum of dropped scores, implying top- k selection minimizes the bound. Across encoders on GLUE and SQuAD, MaskLoRA matches full-model accuracy while yielding 1.3 -- 2.6x end-to-end speedups; on long-context NarrativeQA/GovReport it attains 1.5 -- 3.6x with small but nonzero degradation (e.g., 0.4 at =70%, up to 1.2 at =50%, and up to 2.5 at 30%). Faithfulness improves over strong baselines (sufficiency 99.2%, necessity 59.3%, lower KL at =50%), and energy falls accordingly (GPU Joules-per-correct 3.28 1.34 at 30%). The method is KV-cache compatible, rank- and budget-controllable, and robust under OOD stress (HANS, adversarial sentiment, noisy SQuAD; at moderate budgets (50%) 0.9 abs. drop at matched budgets). Qualitative analyses show layer-wise retention concentrating at semantic bottlenecks and class-separated h i embeddings. MaskLoRA offers accuracy-matched speedups with interpretable rationales and practical deployment hooks.

MaskLoRA: LowвЂ‘Rank SubspaceвЂ“Induced Token Masking for Efficient and Faithful Language Models

A key barrier to interpreting large language models is polysemanticity, where neurons activate for multiple unrelated concepts. Sparse autoencoders (SAEs) have been proposed to mitigate this issue by transforming dense activations into sparse, more interpretable features. While prior work suggests that SAEs promote monosemanticity, no quantitative comparison has examined how concept activation distributions differ between SAEs and their base models. This paper provides the first systematic evaluation of SAEs against base models through activation distribution lens. We introduce a fine-grained concept separability score based on the JensenвЂ“Shannon distance, which captures how distinctly a neuron's activation distributions vary across concepts. Using two large language models (Gemma-2-2B and DeepSeek-R1) and multiple SAE variants across five datasets (including word-level and sentence-level), we show that SAEs reduce polysemanticity and achieve higher concept separability. To assess practical utility, we evaluate concept-level interventions using two strategies: full neuron masking and partial suppression. We find that, compared to base models, SAEs enable more precise concept-level control when using partial suppression. Building on this, we propose Attenuation via Posterior Probabilities (APP), a new intervention method that uses concept-conditioned activation distributions for targeted suppression. APP achieves the smallest perplexity increase while remaining highly effective at concept removal.

Evaluating Sparse Autoencoders for Monosemantic Representation

Access control is a cornerstone of secure computing, yet large language models often blur role boundaries by producing unrestricted responses. We study role-conditioned refusals, focusing on the LLM's ability to adhere to access control policies by answering when authorized and refusing when not. To evaluate this behavior, we created a novel dataset that extends the Spider and BIRD text-to-SQL datasets, both of which have been modified with realistic PostgreSQL role-based policies at the table and column levels. We compare three designs: (i) zero or few-shot prompting, (ii) a two-step generator-verifier pipeline that checks SQL against policy, and (iii) LoRA fine-tuned models that learn permission awareness directly. Across multiple model families, explicit verification (the two-step framework) improves refusal precision and lowers false permits. At the same time, fine-tuning achieves a stronger balance between safety and utility (i.e., when considering execution accuracy). Longer and more complex policies consistently reduce the reliability of all systems. We release RBAC-augmented datasets and code.

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Enabling Autoregressive Models to Fill In Masked Tokens

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