Morocco

In recent years, there has been a resurgence of interest in non-autoregressive text generation in the context of general language modeling. 
Unlike the well-established autoregressive language modeling paradigm, which has a plethora of standard training and inference libraries, implementations of non-autoregressive language modeling have largely been bespoke making it difficult to perform systematic comparisons of different methods. Moreover, each non-autoregressive language model typically requires it own data collation, loss, and prediction logic, making it challenging to reuse common components. In this work, we present the XLM python package, which is designed to make implementing small non-autoregressive language models faster with a secondary goal of providing a suite of small pre-trained models (through a companion \texttt{xlm-models} package) that can be used by the research community.

EACL 2026

XLM: A Python package for non-autoregressive language models

insertion lanuage models

discrete diffusion

non autoregressive text generation

2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

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#### *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. 

Robust and comprehensive evaluation of large language models (LLMs) is essential for identifying the most effective LLM system configurations and mitigating risks associated with deploying LLMs in sensitive domains. However, traditional statistical metrics are poorly suited to open-ended generation tasks, leading to growing reliance on LLM-based evaluation methods. These methods, while often more flexible, introduce additional complexity: they depend on carefully chosen models, prompts, parameters, and evaluation strategies, making the evaluation process prone to misconfiguration and bias. In this work, we present EvalSense, a flexible, extensible framework for constructing domain-specific evaluation suites tailored to custom datasets and tasks. EvalSense provides out-of-the-box support for a broad range of model providers and evaluation strategies, and assists users in selecting and deploying suitable evaluation methods for their specific use-case. This is achieved through two core components: (1) an interactive guide aiding users in evaluation method selection and (2) automated meta-evaluation tools that assess the effectiveness of different evaluation approaches using perturbed data. We demonstrate the effectiveness of EvalSense in a case study involving the generation of clinical notes from unstructured doctor-patient dialogues, using a popular open-source dataset. All code, documentation, and assets associated with EvalSense are open-source and publicly available at https://github.com/nhsengland/evalsense.

EvalSense: A Framework for Domain-Specific LLM (Meta-)Evaluation

Large Language Models (LLMs) frequently generate hallucinated content, posing significant challenges for applications where factuality is crucial. While existing hallucination detection methods typically operate at the sentence level or passage level, we propose FactSelfCheck, a novel zero-resource black-box sampling-based method that enables fine-grained fact-level detection. Our approach represents text as interpretable knowledge graphs consisting of facts in the form of triples, providing clearer insights into content factuality than traditional approaches. Through analyzing factual consistency across multiple LLM responses, we compute fine-grained hallucination scores without requiring external resources or training data. Our evaluation demonstrates that FactSelfCheck performs competitively with leading sentence-level sampling-based methods while providing more detailed and interpretable insights. Most notably, our fact-level approach significantly improves hallucination correction, achieving a 35.5% increase in factual content compared to the baseline, while sentence-level SelfCheckGPT yields only a 10.6% improvement. The granular nature of our detection enables more precise identification and correction of hallucinated content. Additionally, we contribute FavaMultiSamples, a novel dataset that addresses a gap in the field by providing the research community with a second dataset for evaluating sampling-based methods.

FactSelfCheck: Fact-Level Black-Box Hallucination Detection for LLMs

This position paper presents a novel perspective on the utilization of Large Language Models (LLMs) in the artificial intelligence paper review process. We first critique the current tendency for LLMs to be primarily used for simple review text generation, arguing instead that this approach overlooks more meaningful applications of LLMs that preserve human expertise at the core of evaluation. Instead, we advocate for leveraging LLMs to support key aspects of the review processвЂ”specifically, verifying the reproducibility of experimental results, checking the correctness and relevance of citations, and assisting with ethics review flagging. For example, integrating tools based on LLM Agents for code generation from research papers has recently enabled automated assessment of the reproducibility of the paper, thereby improving the transparency and reliability of research. By reorienting LLM usage toward these targeted and assistive roles, we outline a pathway for more effective and responsible integration of LLMs into peer review, ultimately supporting both reviewer efficiency and the integrity of the scientific process.

Position Paper: How Should We Responsibly Adopt LLMs in the Peer Review Process?

Large Language Models (LLMs) display remarkable capabilities to understand or even produce political discourse, but have been found to consistently display a progressive left-leaning bias. At the same time, so-called persona or identity prompts have been shown to produce LLM behavior that aligns with socioeconomic groups that the base model is not aligned with. In this work, we analyze whether zero-shot persona prompting with limited information can accurately predict individual voting decisions and, by aggregation, accurately predict positions of European groups on a diverse set of policies. We evaluate if predictions are stable towards counterfactual arguments, different persona prompts and generation methods. Finally, we find that we can simulate voting behavior of Members of the European Parliament reasonably well with a weighted F1 score of approximately 0.793. Our persona dataset of politicians in the 2024 European Parliament and our code are available at https://anonymous.4open.science/r/european_parliament_simulation-7285.

Persona-driven Simulation of Voting Behavior in the European Parliament with Large Language Models

General-purpose Large Language Models (LLMs) are frequently fine-tuned through supervised fine-tuning (SFT) to enhance performance in specific domains. Better results can be achieved by distilling the chain-of-thought of a larger model at the cost of numerous expensive calls and a much greater amount of data. We propose a novel blueprint for efficient fine-tuning that uses reasoning only for complex data identified by entropy. Specifically, across three small open models (3B) we split the training data into complexity categories by a single token answer entropy (ROC AUC 0.73), fine-tune large language models (LLMs) via SFT and distillation, and show that our pipeline significantly outperforms the standard SFT approach (0.58 vs 0.45 average accuracy) and outperforms the distillation approach (0.58 vs 0.56 average accuracy) while using 81% less data. We publish our code and data to facilitate further research in this direction.

Complexity-aware fine-tuning

We propose a novel K -step return estimation method (called K ETCHUP) for Reinforcement Learning (RL)-based knowledge distillation (KD) in text generation tasks. Our idea is to induce a K -step return by using the Bellman Optimality Equation for multiple steps. Theoretical analysis shows that this K -step formulation reduces the variance of the gradient estimates, thus leading to improved RL optimization, especially when the student model size is large. Empirical evaluation on three text generation tasks demonstrates that our approach yields superior performance in both standard task metrics and large language model (LLM)-based evaluation. These results suggest that our K -step return induction offers a promising direction for enhancing RL-based KD in LLM research.

KETCHUP: KK-Step Return Estimation for Sequential Knowledge Distillation

This paper discusses the internal behavior of Transformer language models. Many recent pre-trained models have been reported to exhibit only slight changes in the angular distance between input and output hidden state vectors in the middle Transformer layers, despite a disproportionately large ``jump'' in angular distance occurring in or around the final Transformer layer. To characterize this, we first introduce a quantitative metric for jump strength around the final layer, and then demonstrate its prevalence across many public pre-trained models, as well as its amplification throughout pre-training. Assuming such jumps indicate an undesirable property, we propose a regularizer that penalizes the jump during pre-training, thereby encouraging more balanced capability usage across the middle layers. Empirical evaluations of three model sizes of Llama-based models, trained with our jump-suppressing regularizer (JREG), reveal improved task performance compared to the baseline without altering the model architecture.

Suppressing Final Layer Hidden State Jumps in Transformer Pretraining

With the increasing adoption of Large Language Models (LLMs) and Vision-Language Models (VLMs), rich document analysis technologies for applications like Retrieval-Augmented Generation (RAG) and visual RAG are gaining significant attention. Recent research indicates that using VLMs yields better RAG performance, but processing rich documents remains a challenge since a single page contains large amounts of information. In this paper, we present SCAN (SemantiC Document Layout ANalysis), a novel approach that enhances both textual and visual Retrieval-Augmented Generation (RAG) systems that work with visually rich documents. It is a VLM-friendly approach that identifies document components with appropriate semantic granularity, balancing context preservation with processing efficiency. SCAN uses a coarse-grained semantic approach that divides documents into coherent regions covering contiguous components. We trained the SCAN model by fine-tuning object detection models on an annotated dataset. Our experimental results across English and Japanese datasets demonstrate that applying SCAN improves end-to-end textual RAG performance by up to 9.4 points and visual RAG performance by up to 10.4 points, outperforming conventional approaches and even commercial document processing solutions.

SCAN: Semantic Document Layout Analysis for Textual and Visual Retrieval-Augmented Generation

Recent advancements in LLMs have significantly improved mathematical problem-solving, with models like GPT-4 achieving human-level performance. However, proficiently solving mathematical problems differs fundamentally from effectively teaching mathematics. To bridge this gap, we introduce the Bi-GSM8K benchmark, a bilingual English-Korean dataset enriched with teacher solutions, student solutions, and annotations marking students' initial errors. This dataset is designed to evaluate two core capabilities of LLMs: (1) measuring similarity between student and teacher solutions, and (2) identifying the initial error point in student solutions. Our method achieves high agreement with human judgments, with Pearson 0.89 and Spearman 0.88 on English, and Pearson 0.89 and Spearman 0.87 on Korean. It also offers significantly lower latency and resource usage than commercial APIs, demonstrating strong computational efficiency. In the error detection task, open-source models achieved approximately 86% accuracy, with performance within 10% points of commercial LLMs API, suggesting strong practical potential. Our key contributions include the open-source release of Bi-GSM8K, novel evaluation metrics, and comparative analyses of LLM performance across languages.

Beyond Accuracy: Alignment and Error Detection across Languages in the Bi-GSM8K Math-Teaching Benchmark

In conversation, humans use multimodal cues, such as speech, gestures, and gaze, to manage turn-taking. While linguistic and acoustic features are informative, gestures provide complementary cues for modeling these transitions. To study this, we introduce DnD Gesture++, an extension of the multi-party DnD Gesture corpus enriched with 2,663 semantic gesture annotations spanning iconic, metaphoric, deictic, and discourse types. Using this dataset, we model turn-taking prediction through a Mixture-of-Experts framework integrating text, audio, and gestures. Experiments show that incorporating semantically guided gestures yields consistent performance gains over baselines, demonstrating their complementary role in multimodal turn-taking.

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EvalSense: A Framework for Domain-Specific LLM (Meta-)Evaluation

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