Morocco

Recent work has explored pruning merges from BPE subword tokenisers using signals from corpus data. We argue that because a BPE tokeniser contains a rich data structure on top of its vocabulary set, this in itself can be used as a guide to modify its merges such that segmentations become more desirable. We apply this argument to one of those pruning algorithms, BPE-knockout, by introducing a new reification step that suggests new merges by inspecting the effects left by pruning. By alternating both processes iteratively until convergence, we get a new BPE tokeniser, ReBPE, which outperforms the original BPE-knockout algorithm on morphological alignment.

EACL 2026

ReBPE: Iteratively Improving the Internal Structure of a Structured Tokeniser by Mining its Internal Structure

segmentation

morphology

tokenization

## Welcome to 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics!
This year, the conference is in Mexico City. NAACL was actually already planned for Mexico City in 2021, but due to the pandemic the entire conference was moved online. This year, finally, we get to go! So it is my sincere pleasure to welcome you to Mexico City, whether in person or virtually. Having the conference in Mexico City is a good opportunity to emphasize that NAACL is our flagship conference for ACL members not only in North America but also in Central and South America, even though NAACL has been bearing “North” in its name. At this year’s conference, we have a theme to match, with a theme track on the Languages of Latin America to showcase the linguistic diversity of the region.
 
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Welcome and hope you all enjoy the conference! 
Katrin Erk 
The University of Texas at Austin 
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NAACL 2024

2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics

poster

#### *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)* 

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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. 

Prior work on attention–syntax alignment has largely focused on single-hop Universal Dependency edges (DPs). In this paper, we treat short multi-hop dependency paths (MDPs) (e.g., “obl+case”) as first-class units and analyze them alongside DPs. Across three pretrained autoregressive LMs (GPT-2 XL, Llama 3 8B, Qwen3-8B) and one encoder baseline (BERT-large), we extract 2--3 hop MDPs from UD-parsed English and quantify head–relation alignment with an Unlabeled Attachment Score (UAS)–style metric modified for causal masking in decoder-only models. Rank visualizations reveal both overlap and specialization: we observe heads that align with both DPs and MDPs, as well as heads that appear specialized for one route. To test functional relevance, we first identify heads by UAS and then apply an undifferentiated (uniform) attention ablation to those heads; we evaluate the impact on BLiMP and LAMBADA. Ablating the top 10% of all heads shows that MDP-selected heads induce larger drops than DP-selected heads and that the union (“Mix”) of DP- and MDP-selected heads yields the largest drops. For GPT-2 XL, the observed drops are (BLiMP: DP = 1.35 pp, MDP = 4.81 pp, Mix = 7.11 pp; LAMBADA: DP = 4.70 pp, MDP = 25.17 pp, Mix = 32.99 pp), all exceeding size-matched random controls. These results indicate that models can route information consistent with syntactic dependencies via both DP and MDP pathways, with MDPs playing a distinct and measurable role in some settings under our interventions.

Revealing Redundant Syntax in Large Language Models through Multi-Hop Dependency Paths

Recent advances in large language models (LLMs) demonstrate their potential as educational tutors. However, different tutoring strategies benefit different student personalities, and mismatches can be counterproductive to student outcomes. Despite this, current LLM tutoring systems do not take into account student personality traits. To address this problem, we first construct a taxonomy that links pedagogical methods to personality profiles, based on pedagogical literature. We simulate student-teacher conversations and use our framework to let the LLM tutor adjust its strategy to the simulated student personality. We evaluate the scenario with human teachers and find that they consistently prefer our approach over two baselines. Our method also increases the use of less common, high-impact strategies such as role-playing, which human and LLM annotators prefer significantly. Our findings pave the way for developing more personalized and effective LLM use in educational applications.

PATS: Personality-Aware Teaching Strategies with Large Language Model Tutors

Negation is a fundamental linguistic phenomenon that poses persistent challenges for Large Language Models (LLMs), particularly in tasks requiring deep semantic understanding. Existing benchmarks often treat negation as a side case within broader tasks like natural language inference, resulting in a lack of benchmarks that exclusively target negation understanding. In this work, we introduce **NUBench**, a novel benchmark explicitly designed to assess sentence-level negation understanding in LLMs. NUBench goes beyond surface-level cue detection by contrasting standard negation with structurally diverse alternatives such as local negation, contradiction, and paraphrase. The benchmark consists of manually curated sentence-negation pairs and a multiple-choice dataset that enables in-depth evaluation of models' negation understanding.

NUBench: A Benchmark for LLMs' Sentence-Level Negation Understanding

Multilingual Fine-tuning of Large Language Models (LLMs) has achieved great advancements in machine translation. However, existing research focuses only on the traditional fine-tuning setting with a fixed set of languages, lacking dynamic adaptability to new ones. Introducing new languages requires retraining and often causes catastrophic forgetting. In this study, we propose a completely modular fine-tuning pipeline that enables dynamic language adaptation for LLMs. Instead of directly fine-tuning on all languages, our approach first trains English-centric input and output LoRA adapters for each language separately, and then merges the corresponding adapters for arbitrary-direction translation without any additional training. Experiments on 12 translation directions of four low-resource and less-supported languages show that modular fine-tuning achieves up to 86% performance of traditional multi-parallel full-parameter fine-tuning, while training only 0.1% parameters and relying solely on English-centric data without any catastrophic forgetting. Furthermore, we perform a comprehensive analysis about the merging ratio, when to merge, and the rationale for using English as a bridge language via Bayesian Optimization and logit lens.

Completely Modular Fine-tuning for Dynamic Language Adaptation

The growing use of large language models (LLMs) has increased the need for automatic evaluation systems, particularly to address the challenge of information hallucination. Although existing faithfulness evaluation approaches have shown promise, they are predominantly English-focused and often require expensive human-labeled training data for fine-tuning specialized models. As LLMs see increased adoption in multilingual contexts, there is a need for accurate faithfulness evaluators that can operate across languages without extensive labeled data. This paper presents STEMF (Self-Taught Evaluators for Multilingual Faithfulness), a framework that learns exclusively from synthetic multilingual data while leveraging cross-lingual transfer learning. Through experiments comparing language-specific and mixed-language fine-tuning approaches, we demonstrate a consistent relationship between an LLM's general language capabilities and its performance in language-specific evaluation tasks. Our framework shows improvements over existing baselines, including state-of-the-art English evaluators and machine translation-based approaches.

Multilingual Self-Taught Faithfulness Evaluators

We present FLUKE (Framework for LingUistically-driven and tasK-agnostic robustness Evaluation), a framework for assessing model robustness through systematic minimal variations of test data. FLUKE introduces controlled variations across linguistic levels --- from orthography to dialect and style --- and leverages large language models (LLMs) with human validation to generate modifications. We demonstrate FLUKE's utility by evaluating both fine-tuned models and LLMs across six diverse NLP tasks (four classification and two generation tasks), and reveal that (1) the impact of linguistic variations is highly task-dependent, with some tests being critical for certain tasks but irrelevant for others; (2) LLMs still exhibit significant brittleness to certain linguistic variations, with reasoning LLMs surprisingly showing less robustness on some tasks compared to base models; (3) models are overall more brittle to natural, fluent modifications such as syntax or style changes (and especially to negation), compared to corruption-style tests such as letter flipping; (4) the ability of a model to use a linguistic feature in generation does not correlate to its robustness to this feature on downstream tasks. These findings highlight the importance of systematic robustness testing for understanding model behaviors.

FLUKE: A Linguistically-Driven and Task-Agnostic Framework for Robustness Evaluation

Large reasoning models (LRMs) increasingly rely on step-by-step Chain-of-Thought (CoT) reasoning to improve task performance, particularly in high-resource languages such as English. While recent work has examined final-answer accuracy in multilingual settings, the thinking traces themselves, i.e., the intermediate steps that lead to the final answer, remain underexplored. In this paper, we present the first comprehensive study of multilingual CoT reasoning, evaluating three key dimensions: performance, consistency, and faithfulness. We begin by measuring language compliance, answer accuracy, and answer consistency when LRMs are explicitly instructed or prompt-hacked to think in a target language, revealing strong language preferences and divergent performance across languages. Next, we assess crosslingual consistency of thinking traces by interchanging them between languages. We find that the quality and effectiveness of thinking traces vary substantially depending on the prompt language. Finally, we adapt perturbation-based techniques -- i.e., truncation and error injection -- to probe the faithfulness of thinking traces across languages, showing that models rely on traces to varying degrees. We will release our code and data to support future research.

A Comprehensive Evaluation of Multilingual Chain-of-Thought Reasoning: Performance, Consistency, and Faithfulness Across Languages

Question generation plays an important role in educational applications, enabling automated assessment and reading comprehension support. Attribute-controlled question generation aims to produce questions that fit predefined characteristics such as difficulty, focus, or coverage. Existing methods predominantly rely on supervised fine-tuning, which often fails to impose a strong adherence to attribute values, resulting in weak coupling between prompt specifications and model outputs. We introduce Odds-Ratio Steerable Optimization (ORSO), a framework designed to enhance attribute sensitivity in question generation models. Building upon preference-based learning techniques without requiring human-curated preference sets, ORSO employs input-level perturbations to create contrastive training signals. Empirical evaluations on both exhaustive and expert-validated attribute configurations indicate that ORSO performs better in enforcing attribute conformity while maintaining output quality. These results argue for the benefits of explicit attribute-aware optimization in controllable question generation tasks.

ORSO QGen: Odds-Ratio Steerable Optimization for Controlling Question Generation

Existing Chinese preference datasets suffer from limited scale, restricted domain coverage, and insufficiently rigorous data validation. Human annotation significantly limits the scalability of human preference datasets. As a result, Chinese Alignment and Chinese Reward Models (CRM) have not yet been thoroughly explored. To address these challenges, we design an LLM-based data annotation pipeline with no human intervention. Based on this pipeline, we curate COIG-P (Chinese Open Instruction Generalist - Preference), a high-quality, large-scale Chinese preference dataset consisting of 1M Chinese preference pairs and 92k carefully curated Chinese queries across diverse domains, including Chat, Coding, Maths, and others. We conduct experiments to verify the quality of COIG-P from two perspectives. (1) COIG-P brings significant performance improvements for the Qwen2/2.5 and Infinity-Instruct model series on AlignBench through DPO, with gains ranging from 2% to 12%. Furthermore, it significantly outperforms other existing Chinese preference datasets. (2) We train an 8B-sized CRM and manually annotate a Chinese Reward Benchmark (CRBench). Our CRM demonstrates robust scoring ability on CRBench. In addition, in practical data construction experiments, the quality of the data constructed by our CRM is comparable to that produced by GPT-4o.

COIG-P: A High-Quality and Large-Scale Chinese Preference Dataset for Alignment with Human Values

Retrieval-Augmented Generation (RAG) has emerged as a standard framework for knowledge-intensive NLP tasks, combining large language models (LLMs) with document retrieval from external corpora. Despite its widespread use, most RAG pipelines continue to treat retrieval and reasoning as isolated componentsвЂ”retrieving documents once and then generating answers without further interaction. This static design often limits performance on complex tasks that require iterative evidence gathering or high-precision retrieval. Recent work in both the information retrieval (IR) and NLP communities has begun to close this gap by introducing adaptive retrieval and ranking methods that incorporate feedback. In this survey, we present a structured overview of advanced retrieval and ranking mechanisms that integrate such feedback. We categorize feedback signals based on their source and role in improving the query, retrieved context, or document pool. By consolidating these developments, we aim to bridge IR and NLP perspectives and highlight retrieval as a dynamic, learnable component of end-to-end RAG systems.

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Revealing Redundant Syntax in Large Language Models through Multi-Hop Dependency Paths

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BPE-knockout: Pruning Pre-existing BPE Tokenisers with Backwards-compatible Morphological Semi-supervision

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