Morocco

Large Language Models (LLMs) are powerful candidates for complex decision-making, leveraging vast encoded knowledge and remarkable zero-shot abilities. However, their adoption in high-stakes environments is hindered by their opacity; their outputs lack faithful explanations and cannot be effectively contested to correct errors, undermining trustworthiness. In this paper, we propose ART (Adaptive Reasoning Trees), a hierarchical method for claim verification. The process begins with a root claim, which branches into supporting and attacking child arguments. An argument&#39;s strength is determined bottom-up via a pairwise tournament of its children, adjudicated by a judge LLM, allowing a final, transparent and contestable verdict to be systematically derived which is missing in methods like Chain-of-Thought (CoT). We empirically validate ART on multiple datasets, analyzing different argument generators and comparison strategies. Our findings show that ART&#39;s structured reasoning outperforms strong baselines, establishing a new benchmark for explainable claim verification which is more reliable and ensures clarity in the overall decision making step.

EACL 2026

ART: Adaptive Reasoning Trees for Explainable Claim Verification

llm reasoning

responsible ai

transparency

**Welcome to EMNLP 2023!**

On behalf of the EMNLP 2023 Organizing Committee, I extend a warm and heartfelt welcome to all of you to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP). It is with immense pleasure and excitement that we gather here in Singapore, a vibrant hub of innovation and technological advancement.

The conference program is packed with insightful presentations, thought-provoking workshops, and engaging networking opportunities. In addition to the technical sessions, we have also planned several social events that will provide you with the opportunity to connect with your colleagues.

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Yuji Matsumoto (RIKEN AIP) 
EMNLP 2023 General Chair

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EMNLP 2023

EMNLP 2023 took place in Singapore from Dec 6th to Dec 10th, 2023.

Large Language Models (LLMs) are powerful candidates for complex decision-making, leveraging vast encoded knowledge and remarkable zero-shot abilities. However, their adoption in high-stakes environments is hindered by their opacity; their outputs lack faithful explanations and cannot be effectively contested to correct errors, undermining trustworthiness. In this paper, we propose ART (Adaptive Reasoning Trees), a hierarchical method for claim verification. The process begins with a root claim, which branches into supporting and attacking child arguments. An argument's strength is determined bottom-up via a pairwise tournament of its children, adjudicated by a judge LLM, allowing a final, transparent and contestable verdict to be systematically derived which is missing in methods like Chain-of-Thought (CoT). We empirically validate ART on multiple datasets, analyzing different argument generators and comparison strategies. Our findings show that ART's structured reasoning outperforms strong baselines, establishing a new benchmark for explainable claim verification which is more reliable and ensures clarity in the overall decision making step.

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. 

Despite advances in multilingual automatic speech recognition (ASR), code-switching (CS), the mixing of languages within an utterance common in daily speech, remains a severely underexplored challenge. In this paper, we introduce HiKE: the Hierarchical Korean-English code-switching benchmark, the first globally accessible evaluation framework for Korean-English CS, aiming to provide a means for the precise evaluation of multilingual ASR models and to foster research in the field. The proposed framework not only consists of high-quality, natural CS data across various topics, but also provides meticulous loanword labels and a hierarchical CS-level labeling scheme (word, phrase, and sentence) that together enable a systematic evaluation of a model's ability to handle each distinct level of code-switching. Through evaluations of diverse multilingual ASR models and fine-tuning experiments, this paper demonstrates that while most multilingual ASR models initially struggle with CS-ASR, this capability can be enabled through fine-tuning with both natural and synthetic CS data.

HiKE: Hierarchical Evaluation Framework for Korean-English Code-Switching Speech Recognition

We present a novel approach to bias mitigation in large language models (LLMs) by applying steering vectors to modify model activations in forward passes. We compute 8 steering vectors, each corresponding to a different social bias axis, such as age, gender, or race, on a training subset of the BBQ dataset and compare the effectiveness of these to 3 additional bias mitigation methods across 4 datasets. When optimized on the BBQ dataset, our individually tuned steering vectors achieve average improvements of 12.8% on BBQ, 8.3% on CLEAR-Bias, and 1% on StereoSet, and show improvements over prompting and Self-Debias in all cases, and improvements over fine-tuning in 12 out of 17 evaluations. In addition, steering vectors showed the lowest impact on MMLU scores of the four bias mitigation methods tested. The work presents the first systematic investigation of steering vectors for bias mitigation, and we demonstrate that they are a powerful and computationally efficient strategy for reducing bias in LLMs, with broader implications for enhancing AI safety.

Shifting Perspectives: Steering Vectors for Robust Bias Mitigation in LLMs

In today's rapidly expanding data landscape, knowledge extraction from unstructured text is vital for real-time analytics, temporal inference, and dynamic memory frameworks. However, traditional static knowledge graph (KG) construction often overlooks the dynamic and time-sensitive nature of real-world data, limiting adaptability to continuous changes. Moreover, recent zero- or few-shot approaches that avoid domain-specific fine-tuning or reliance on prebuilt ontologies often suffer from instability across multiple runs, as well as incomplete coverage of key facts. To address these challenges, we introduce ATOM (AdapTive and OptiMized), a few-shot and scalable approach that builds and continuously updates Temporal Knowledge Graphs (TKGs) from unstructured texts. ATOM splits input documents into minimal, self-contained вЂњatomicвЂќ facts, improving extraction exhaustivity and stability. From these atomic facts, atomic KGs are derived and then merged in parallel. Empirical evaluations demonstrate that ATOM maintains high exhaustivity, achieves stability across multiple runs, and outperforms baseline methods while ensuring low latency and high scalability for large-scale deployments.

ATOM: AdapTive and OptiMized dynamic temporal knowledge graph construction using LLMs

The impact of human label variation (HLV) on model fairness is an unexplored topic. This paper examines the interplay by comparing training on majority-vote labels with a range of HLV methods. Our experiments show that without explicit debiasing, HLV training methods have a positive impact on fairness.

On the Interplay between Human Label Variation and Model Fairness

Biomedical Named Entity Recognition (NER) consists of identifying and classifying important biomedical entities mentioned in text. Traditionally, biomedical NER has heavily relied on domain-specific pre-trained language models; particularly variant of BERT models. With the emergence of large language models (LLMs), some studies have evaluated their performance on biomedical NLP tasks. These studies consistently show that, despite their general capabilities, LLMs still fall short compared to specialized BERT-based models for biomedical NER. However, as LLMs continue to advance at a remarkable pace, natural questions arise: Are they still far behind, or are they starting to be competitive? In this study, we investigate the performance of recent LLMs across multiple biomedical NER datasets under both clean and noisy dataset conditions. Our findings reveal that LLMs are progressively closing the performance gap with BERT-based models and demonstrate particular strengths in low-data settings. Moreover, our results suggest that in-context learning with LLMs exhibits a notable degree of robustness to noise, making them a promising alternative in settings where labeled data is scarce or noisy.

Where do LLMs currently stand on biomedical NER in both clean and noisy settings ?

This study investigates the internal information flow of large language models (LLMs) while performing chain-of-thought (CoT) style reasoning. Specifically, with a particular interest in the faithfulness of the CoT explanation to LLMs' final answer, we explore (i) when the LLMs' answer is (pre)determined, especially before the CoT begins or after, and (ii) how strongly the information from CoT specifically has a causal effect on the final answer. Our experiments with controlled arithmetic tasks reveal a systematic internal reasoning mechanism of LLMs. They have not derived an answer at the moment when input was fed into the model. Instead, they compute (sub-)answers while generating the reasoning chain on the fly. Therefore, the generated reasoning chains can be regarded as faithful reflections of the model's internal computation.

LLMs Faithfully and Iteratively Compute Answers During CoT: A Systematic Analysis With Multi-hop Arithmetics

With the growing use of large language models (LLMs) as evaluators, their application has expanded to code evaluation tasks, where they assess the correctness of generated code without relying on reference implementations. While this offers scalability and flexibility, it also raises a critical, unresolved question: Can LLM judges fairly and robustly evaluate semantically equivalent code with superficial variations? Functionally correct code often exhibits variationsвЂ”such as differences in variable names, comments, or formattingвЂ”that should not influence its correctness. Yet, whether LLM judges can reliably handle these variations remains unclear. We present the first comprehensive study of this issue, defining six types of potential bias in code evaluation and revealing their systematic impact on LLM judges. Across five programming languages and multiple LLMs, we empirically demonstrate that all tested LLM judges are susceptible to both positive and negative biases, resulting in inflated or unfairly low scores. Moreover, we observe that LLM judges remain vulnerable to these biases even when prompted to generate test cases before scoring, highlighting the need for more robust code evaluation method

Don’t Judge Code by Its Cover: Exploring Biases in LLM Judges for Code Evaluation

Natural Language Processing (NLP) is integral to social media analytics but often processes content containing Personally Identifiable Information (PII), behavioral cues, and metadata raising privacy risks such as surveillance, profiling, and targeted advertising. To systematically assess these risks, we review 203 peer-reviewed papers and propose the~NLP Privacy Risk Identification in Social Media (NLP-PRISM) framework, which evaluates vulnerabilities across six dimensions: data collection, preprocessing, visibility, fairness, computational risk, and regulatory compliance. Our analysis shows that transformer models achieve F1-scores ranging from 0.58 – 0.84, but incur a 1% - 23% drop under privacy-preserving fine-tuning. Using NLP-PRISM, we examine privacy coverage in six NLP tasks: sentiment analysis (16), emotion detection (14), offensive language identification (19), code-mixed processing (39), native language identification (29), and dialect detection (24) revealing substantial gaps in privacy research. We further found a (2%-9%) trade-off in model utility, MIA AUC (membership inference attacks) 0.81, AIA accuracy 0.75 (attribute inference attacks). Finally, we advocate for stronger anonymization, privacy-aware learning, and fairness-driven training to enable ethical NLP in social media contexts.

NLP Privacy Risk Identification in Social Media (NLP-PRISM): A Survey

Large Language Models (LLMs) are increasingly being used to understand how scientific research evolves, drawing growing interest from the research community. However, limited work has explored the scientific fact-checking of research questions and claims from manuscripts, particularly within the NLP domain, an emerging direction for advancing scientific integrity and knowledge validation. In this work, we propose a novel scientific fact-checking dataset, SCINLP, tailored to the NLP domain. Our proposed framework on SCINLP systematically verifies the veracity of complex scientific research questions across varying rationale contexts, while also assessing their temporal positioning. SCINLP includes supporting and refuting research questions from a curated collection of influential and reputed NLP papers published between 2000 and 2024. In our framework, we use multiple LLMs and diverse rationale contexts from our dataset to examine scientific claims and research focus, complemented by feasibility judgments for deeper insight into scientific reasoning in NLP.

Pushing the Frontiers of Scientific Fact-Checking: The SCINLP Dataset

Recent advances in large language models (LLMs) have introduced explicit reasoning capabilities, yet the factors that truly drive their improved performance remain unclear. In this work, we disentangle the effects of reasoning quality and sequence length by fine-tuning 8B models on several Polish variants of the Mixture-of-Thoughts (MoT-PL) dataset, each representing a distinct reasoning style: Detailed, Summarized, BabyThink, Lengthy. We found that the model trained on high-quality reasoning traces achieved better average performance than all other models; neither longer reasoning with similar quality nor low-quality reasoning with similar length achieved similar gains. Qualitative and quantitative analyses further reveal that reasoning clarity, rather than verbosity, determines model performance. These findings underscore the importance of reasoning content quality in LLM training and provide new insights into designing more effective reasoning-oriented datasets and models.

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Next from EACL 2026

HiKE: Hierarchical Evaluation Framework for Korean-English Code-Switching Speech Recognition

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Explainable Claim Verification via Knowledge-Grounded Reasoning with Large Language Models

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