Morocco

Grouping, e.g., grouping channels, which is widely used in current integer-based quantization, has become essential for the emerging MXFP4 format. Ideally, each group should contain channels with similar quantization scales. To guide such groups, existing work clusters the channels using scalar proxy, ignoring the token dimension, which we find suboptimal. In this paper, we propose TAGQuant, a simple yet powerful enhancement for such ``group-wise&#39;&#39; quantization. By strategically shuffling channels to group those with similar token-wise activation distributions, TAGQuant ensures better clustering of large- and small-range values. This shuffle operation is hardware-efficient, and seamlessly integrated into the quantization process with only 0.01x latency overhead. TAGQuant reduces relative GSM8K error in both INT4 and MXFP4 formats, by up to 86\% in Llama-3.1-8B-Instruct compared to baselines, validating the effectiveness of our channel shuffling approach for group-wise quantization. Code is publicly available.

EACL 2026

TAGQuant: Token-Aware Clustering for Group-Wise Quantization

mxfp4

4-bit quantization

group-wise quantization

Grouping, e.g., grouping channels, which is widely used in current integer-based quantization, has become essential for the emerging MXFP4 format. Ideally, each group should contain channels with similar quantization scales. To guide such groups, existing work clusters the channels using scalar proxy, ignoring the token dimension, which we find suboptimal. In this paper, we propose TAGQuant, a simple yet powerful enhancement for such ``group-wise'' quantization. By strategically shuffling channels to group those with similar token-wise activation distributions, TAGQuant ensures better clustering of large- and small-range values. This shuffle operation is hardware-efficient, and seamlessly integrated into the quantization process with only 0.01x latency overhead. TAGQuant reduces relative GSM8K error in both INT4 and MXFP4 formats, by up to 86\% in Llama-3.1-8B-Instruct compared to baselines, validating the effectiveness of our channel shuffling approach for group-wise quantization. Code is publicly available.

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. 

Argument mining (AM) involves extracting argument components and predicting relations between them to create argumentative graphs, which are essential for applications requiring argumentative comprehension. To automatically provide high-quality graphs, previous works require a large amount of human-annotated training samples to train AM models. Instead, we leverage a large language model (LLM) to assign pseudo-labels to training samples for reducing reliance on human-annotated training data. However, the training data weakly-labeled by the LLM are too noisy to develop an AM model with reliable performance. In this paper, to improve the model performance, we propose a center-based component detector that refines the boundaries of the detected components and a relation denoiser to deal with noise present in the pseudo-labels when classifying relations between detected components. Experimentally, our AM model improves the boundary detection obtained from the LLM by up to16 % in terms of IoU75 and of the relation classification obtained from the LLM by up to 12% in terms of macro-F1 score. Our AM model achieves new state-of-the-art performance in weakly-supervised AM, showing up to a 6% improvement over the state-of-the-art component detector and up to a 7% improvement over the state-of-the-art relation classifier. Additionally, our model uses less than 20% of human-annotated data to match the performance of state-of-the-art fully-supervised AM models.

Weakly-supervised Argument Mining with Boundary Refinement and Relation Denoising

Small language models (sLLMs) are increasingly deployed on-device, where imperfect user prompts--typos, unclear intent, or missing context--can trigger factual errors and hallucinations. Existing automatic prompt optimization (APO) methods were designed for large cloud LLMs and rely on search that often produces long, structured instructions; when executed under an on-device constraint where the same small model must act as optimizer and solver, these pipelines can waste context and even hurt accuracy. We propose POaaS, a minimal-edit prompt optimization layer that routes each query to lightweight specialists (Cleaner, Paraphraser, Fact-Adder) and merges their outputs under strict drift and length constraints, with a conservative skip policy for well-formed prompts. Under a strict fixed-model setting with Llama-3.2-3B and Llama-3.1-8B, POaaS improves both task accuracy and factuality while representative APO baselines degrade them, and POaaS recovers up to +7.4% under token deletion and mixup. Overall, per-query conservative optimization is a practical alternative to search-heavy APO for on-device sLLMs.

POaaS: Minimal-Edit Prompt Optimization as a Service to Lift Accuracy and Cut Hallucinations on On-Device sLLMs

Knowledge graphs like DBpedia enable structured fact verification, but the relative contributions of symbolic structure, neural semantics, and evidence grounding remain unclear. We present a systematic study on FACTKG (108,675 claims) comparing symbolic, neural, and LLM-based approaches. Our symbolic baseline using 29 hand-crafted features covering graph structure, entity coverage, and semantic relation type achieves 66.54% accuracy, while BERT over linearized subgraphs reaches 92.68% and graph neural networks plateau at 70%, demonstrating that token-level semantics outperform both symbolic features and message passing. Using GPT-4.1-mini to filter training data, budget-matched controls show that token-budget control recovers most of the gap over truncation-dominated inputs, while LLM semantic selection adds +1.31 points beyond lexical heuristics (78.85% filtered vs. 77.54% heuristic vs. 52.70% unfiltered), showing that semantic relevance, not just evidence quantity, governs learnability. Finally, comparing 300 test claims under memorization (claim-only) versus KG-grounded reasoning with chain-of-thought, we find KG grounding improves GPT-4o-mini and GPT-4.1-mini accuracy by 12.67 and 9.33 points respectively, with models citing specific triples for interpretability. These results demonstrate that neural semantic representations and explicit KG evidence grounding are highly effective for robust, interpretable fact verification.



Evidence Grounding vs. Memorization: Why Neural Semantics Matter for Knowledge Graph Fact Verification

utomated fact verification systems typically assume static truth values, overlooking that many real-world claims are time-dependent. We introduce Temporal Fact Verification (TFV), a framework that explicitly models temporal dynamics through timestamp aware evidence retrieval and temporal entailment reasoning. Our contributions include: (1) TempFact, a dataset of 15,423 claims annotated with temporal validity intervals across static, dynamic, and underspecified categories; (2) a novel pipeline integrating temporal signal extraction, learned temporal embeddings, and joint semantic-temporal reasoning; and (3) comprehensive experiments showing TFV achieves 78.3% accuracy on dynamic claims, significantly outperforming state-of-the-art baselines (p < 0.001). Our work establishes temporal reasoning as essential for fact verification in evolving information environments.

Temporal Fact Verification: Timestamp-Aware Evidence Retrieval and Temporal Entailment for Time-Evolving Claims

Large Language Models (LLMs) frequently "hallucinate" plausible but incorrect assertions, a vulnerability often missed by uncertainty metrics when models are "confidently wrong." We propose DiffuTruth, an unsupervised framework that re-conceptualizes fact verification via non-equilibrium thermodynamics, positing that factual truths act as stable attractors on a generative manifold while hallucinations are unstable. We introduce the "Generative Stress Test": claims are corrupted with noise and reconstructed using a discrete text diffusion model. We define Semantic Energy, a metric measuring the semantic divergence between the original claim and its reconstruction using an NLI critic. Unlike vector-space errors, Semantic Energy isolates deep factual contradictions. We further propose a Hybrid Calibration fusing this stability signal with discriminative confidence. Extensive experiments on FEVER demonstrate DiffuTruth achieves a state-of-the-art unsupervised AUROC of 0.725, outperforming baselines by +1.5% through the correction of overconfident predictions. Furthermore, we show superior zero-shot generalization on the multi-hop HOVER dataset, outperforming baselines by over 4%, confirming the robustness of thermodynamic truth properties to distribution shifts.

The Energy of Falsehood: Detecting Hallucinations via Diffusion Model Likelihoods

Automated fact-checking in dialogue involves multi-turn conversations where colloquial language is frequent yet understudied. To address this gap, we propose a conservative rewrite candidate for each response claim via staged de-colloquialisation, combining lightweight surface normalisation with scoped in-claim coreference resolution. We then introduce BiCon-Gate, a semantics-aware consistency gate that selects the rewrite candidate only when it is semantically supported by the dialogue context, otherwise falling back to the original claim. On the DialFact benchmark, this gated selection stabilises downstream fact-checking and yields gains in both evidence retrieval and fact verification—particularly strong gains on SUPPORTS—and outperforms competitive baselines, including a decoder-based one-shot LLM rewrite that attempts to perform all de-colloquialisation steps in a single pass.

BiCon-Gate: Consistency-Gated De-colloquialisation for Dialogue Fact-Checking

Multimodal fact checking has become increasingly important due to the predominance of visual content on social media platforms, where images are frequently used to enhance the credibility and spread of misleading claims, while generated images become more prevalent and realistic as generative models advance. Incorporating visual information, however, substantially increases computational costs, raising critical efficiency concerns for practical deployment. In this study, we propose and evaluate the ADA-AGGR (ensemble retrievAl for multimoDAl evidence AGGRegation) pipeline, which achieved the second place on both the dev and test leaderboards of the FEVER 9/AVerImaTeC shared task. However, long runtimes per claim highlight challenges regarding efficiency concerns when designing multimodal claim verification pipelines. We therefore run extensive ablation studies and configuration analyses to identify possible performance–runtime improvements. Our experiments show that substantial efficiency gains are possible without significant loss in verification quality. For instance, we reduced the average runtime by up to 6.28× while maintaining comparable performance across evaluation metrics by aggressively downsampling input images processed by visual language models. Overall, our results highlight that careful design choices are crucial for building scalable and resource-efficient multimodal fact-checking systems suitable for real-world deployment.

Take It All: Ensemble Retrieval for Multimodal Evidence Aggregation

This paper describes VILLAIN, a multimodal fact-checking system that verifies image-text claims through prompt-based multi-agent collaboration. For the AVerImaTeC shared task, VILLAIN employs vision-language model agents across multiple stages of fact-checking. Textual and visual evidence is retrieved from the knowledge store enriched through additional web collection. To identify key information and address inconsistencies among evidence items, modality-specific and cross-modal agents generate analysis reports. In the subsequent stage, question-answer pairs are produced based on these reports. Finally, the Verdict Prediction agent produces the verification outcome based on the image-text claim and the generated question-answer pairs. Our system ranked first on the leaderboard across all evaluation metrics. The source code is publicly available at https://github.com/ssu-humane/VILLAIN.

VILLAIN at AVerImaTeC: Verifying Image–Text Claims via Multi-Agent Collaboration

Today, search is used by LLMs to support retrieval-augmented generation as well as by humans. In this new setting, how does what we want from a search engine change, and what are promising approaches to take? I will describe work on two themes related to these questions: (1) retrieving documents to support complex information needs in the context of long-form retrieval-augmented generation and (2) exploring learned sparse retrieval as a promising paradigm for first-stage retrieval. In the former line of work, I will discuss search systems aimed at maximizing coverage of relevant information and approaches for evaluating their ability to do so independent of a generation system. In the latter line of work, I will describe research positioning learned sparse retrieval as a compelling alternative to dense retrieval.

Search with Complex Topics and Learned Sparse Retrieval

Language is one of the richest and most complex human cognitive capacities. Yet, we lack a model organism to study its underlying neural mechanisms: unlike other important cognitive capacities, such as vision or memory, language does not have a clear counterpart in non-human animals, leaving a gap in our ability to develop and test mechanistic hypotheses. In recent years, large language models (LLMs) have emerged as the closest computational analogs we have but how can we use them effectively as model organisms for language in the human brain? In this talk, I will discuss the promise and challenges of this approach. I will present our recent work on brain-tuning using naturalistic brain recordings to refine LLMs so that their internal representations and processing better align with human neural data. But beyond representational similarity, a key question remains: do LLMs rely on mechanisms that are similar to those in the brain? And at what level of abstraction should we assess this similarity? This research direction aims to transform LLMs from mere engineering artifacts into powerful scientific tools for uncovering how the brain supports our most distinctive cognitive ability.

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Weakly-supervised Argument Mining with Boundary Refinement and Relation Denoising

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