Morocco

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.

EACL 2026

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

explanation faithfulness

multilingual evaluation

chain-of-thought

reasoning

## 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
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EMNLP 2025

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

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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.
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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. 

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.

Test-time Corpus Feedback: From Retrieval to RAG

Self-attention mechanisms in transformers enable tokens to interact across a sequence but lack an explicit inductive bias to capture local contextual dependencies, an inherent characteristic of human languages. We propose Token-Wise Kernels (TWiKers), a novel enhancement to transformers that learn token-specific convolutional kernels applied to the keys or values. Each token is assigned a small kernel, initialized to the "Central Dirac" (e.g., [0,1,0] for size=3), meaning the token "bears" the attention from all other tokens alone. During training, these kernels adapt, and greater deviation from the Central Dirac indicates stronger attention redistribution to neighboring tokens. This introduces the first transformer weights encoding lexical redistribution patterns in a directly interpretable form, without additional post-hoc analysis. Our experiments show that content words (e.g., nouns and verbs) retain self-focus, while function words (e.g., prepositions and conjunctions) shift attention toward their neighbors, aligning with their syntactic and semantic roles. We further apply TWiKers to distinguish literary genres, historical periods, and authors, demonstrating their effectiveness in capturing high-level stylistic patterns. Finally, we demonstrate the potential of TWiKers as an effective inductive bias to improve transformer training, validated across a range of downstream tasks.

Token-Wise Kernels (TWiKers) for Vicinity-Aware Attention in Transformers

Generative retrieval (GR) differs from the traditional index–then–retrieve pipeline by storing relevance in model parameters and generating retrieval cues directly from the query, but it can be brittle out of domain and expensive to scale. We introduce QueStER (QUEry SpecificaTion for gEnerative Keyword-Based Retrieval), which bridges GR and query reformulation by learning to generate explicit keyword-based search specifications. Given a user query, a lightweight LLM produces a keyword query that is executed by a standard retriever (BM25), combining the generalization benefits of generative query rewriting with the efficiency and scalability of lexical indexing. We train the rewriting policy with reinforcement learning techniques. Across in- and out-of-domain evaluations, QueStER consistently improves over BM25 and is competitive with neural IR baselines, while maintaining strong efficiency.

QueStER: Query Specification for Generative Keyword-Based Retrieval

Multi-Agent Debate (MAD) frameworks improve factual reliability in large language models (LLMs) by allowing agents to critique and refine one another’s reasoning. Yet, existing MAD systems are computationally expensive and prone to degradation under prolonged debates due to redundant exchanges and unstable judging. We propose a lightweight,industry-deployable alternative that unifies Selective Debate Initiation (SDI) with Evidence Weighted Self-Consistency (EWSC) for adaptive, debate-on-demand reasoning. SDI dynamically predicts when debate is necessary by detecting confidence-likelihood misalignment and semantic disagreement, skipping well-aligned queries to conserve computation. EWSC replaces a single-judge verdict with a variance-aware, evidence-weighted aggregation across paraphrased evaluations, yielding more stable factual judgments. Combined, SDI and EWSC reduce token consumption by nearly 50% while improving both accuracy and calibration. Evaluated on BoolQ, CosmosQA, and an internal QnA benchmark, our framework achieves higher factual robustness and efficiency, demonstrating that scalable, epistemically reliable multi-agent reasoning is practical for real-world LLM deployments.

SELENE: Selective and Evidence-Weighted LLM Debating for Efficient and Reliable Reasoning

Hierarchical text classification (HTC) depends on taxonomies that organize labels into structured hierarchies. However, many real-world taxonomies introduce ambiguities, such as identical leaf names under similar parent nodes, which prevent language models (LMs) from learning clear decision boundaries. In this paper, we present TaxMorph, a framework that uses large language models (LLMs) to transform entire taxonomies through operations such as renaming, merging, splitting, and reordering. Unlike prior work, our method revises the full hierarchy to better match the semantics encoded by LMs. Experiments across three HTC benchmarks show that LLM-refined taxonomies consistently outperform human-curated ones in various settings up to +2.9pp. in F1. To better understand these improvements, we compare how well LMs can assign leaf nodes to parent nodes and vice versa across human-curated and LLM-refined taxonomies. We find that human-curated taxonomies lead to more easily separable clusters in embedding space. However, the LLM-refined taxonomies align more closely with the model's actual confusion patterns during classification. In other words, even though they are harder to separate, they better reflect the model's inductive biases. These findings suggest that LLM-guided refinement creates taxonomies that are more compatible with how models learn, improving HTC performance.

Hierarchical Text Classification with LLM-Refined Taxonomies

While large language models (LLMs) are generally considered proficient in generating language, how similar their language usage is to that of humans remains understudied. In this paper, we test whether models exhibit linguistic convergence, a core pragmatic element of human language communication, asking: do models adapt, or converge, to the linguistic patterns of their user? To answer this, we systematically compare model completions of exisiting dialogues to the original human responses across sixteen language models, three dialogue corpora, and a variety of stylometric features. We find that models strongly converge to the conversation's style, often significantly overfitting relative to the human baseline. While convergence patterns are often feature-specific, we observe consistent shifts in convergence across modeling settings, with instruction-tuned and larger models converging less than their pretrained counterparts. Given the differences between human and model convergence patterns, we hypothesize that the underlying mechanisms for these behaviors are very different.

Do language models accommodate their users? A study of linguistic convergence

Large language models demonstrate promising long-context processing capabilities, with recent models touting context windows close to one million tokens. However, the evaluations supporting these claims often involve simple retrieval tasks or synthetic tasks padded with irrelevant text, which models may easily detect and discard. In this work, we generate lengthy, simplified English text with first-order logic representations spanning up to 2048 sentences (~25 k GPT-4 tokens). We formulate an evaluation task with evidence retrieval for contradiction detection. The long, homogeneous text is filled with distractors that are both hard to distinguish from relevant evidence and provably non-interfering. Our evaluation of evidence retrieval reveals that the effective context window is much smaller with such realistic distractors, already crumbling at 128 sentences.

Logic Haystacks: Probing LLMs' Long-Context Logical Reasoning (Without Easily Identifiable Unrelated Padding)

Despite major advances in multilingual modeling, large quality disparities persist across languages. Besides the obvious role of training data representation, typological properties have also been proposed to determine the intrinsic difficulty of modeling a language. The existing evidence, however, is mostly based on small monolingual language models or bilingual translation models trained from scratch. We expand on this line of work by analyzing two large pre-trained multilingual translation models, NLLB-200 and Tower+, which are state-of-the-art representatives of encoder-decoder and decoder-only machine translation, respectively. Based on a broad set of languages, we find that target language typology drives translation quality of the NLLB model, even after controlling for more trivial factors, such as data resourcedness and writing script. Additionally, languages with certain typological properties benefit more from a wider search of the output space, suggesting that such languages could benefit from alternative decoding strategies beyond the standard left-to-right beam search. By contrast, variations in translation quality of the LLM-based Tower+ model remain largely unexplained by our predictors. To facilitate further research in this area, we release a set of fine-grained typological properties for 212 languages of the FLORES+ MT evaluation benchmark.

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

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