Morocco

This presentation introduces MADIAVE, a fully zero-shot multi-agent debate framework for implicit attribute value extraction (AVE) in multimodal e-commerce, where multiple MLLM agents iteratively refine predictions by exchanging answers and rationales across rounds. Evaluated on ImplicitAVE (1,610 samples across 5 domains), MADIAVE consistently improves over single-model inference, and under a compute-matched setting, it outperforms majority vote via structured reasoning exchange. Key findings show that 1–2 debate rounds capture most gains (more rounds can drift), low-accuracy attributes benefit most, and a practical default is 2 agents × 2 rounds. 

EACL 2026

MADIAVE: Multi-Agent Debate for Implicit Attribute Value Extraction

debate

large language model

attribute value extraction

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

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*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.* 
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ACL 2025

Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

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)* 

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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. 

Large Language Models (LLMs) excel at many reasoning tasks but struggle with knowledge-intensive queries due to their inability to dynamically access up-to-date or domain-specific information. Retrieval-Augmented Generation (RAG) has emerged as a promising solution, enabling LLMs to ground their responses in external sources. However, existing RAG methods lack fine-grained control over both the query and source sides, often resulting in noisy retrieval and shallow reasoning. In this work, we introduce \textit{\textbf{DeepSieve}}, an agentic RAG framework that incorporates information sieving via LLM-as-a-knowledge-router. DeepSieve decomposes complex queries into structured sub-questions and recursively routes each to the most suitable knowledge source, filtering irrelevant information through a multi-stage distillation process. Our design emphasizes modularity, transparency, and adaptability, leveraging recent advances in agentic system design. Experiments on multi-hop QA tasks across heterogeneous sources demonstrate improved reasoning depth, retrieval precision, and interpretability over conventional RAG approaches.

DeepSieve: Information Sieving via LLM-as-a-Knowledge-Router

Haptic captioning is the task of generating natural language descriptions from haptic signals, such as vibrations, for use in virtual reality and rehabilitation applications. While previous multimodal research has focused primarily on vision and audio, haptic feedback for the sense of touch remain underexplored. To address this gap, we formalize the haptic captioning task and propose HapticLLaMA, a multimodal sensory language model that interprets vibration signals into descriptions in a given sensory, emotional, or associative category. We investigate two types of haptic tokenizers, a frequency-based tokenizer and an EnCodec-based tokenizer, that convert haptic signals into sequences of discrete units, enabling their integration with the LLaMA model. HapticLLaMA is trained in two stages: (1) supervised fine-tuning using the LLaMA architecture with LoRA-based adaptation, and (2) fine-tuning via reinforcement learning from human feedback (RLHF). We assess HapticLLaMA's captioning performance using both automated n-gram metrics and human evaluation. HapticLLaMA demonstrates strong capability in interpreting haptic vibration signals, achieving a METEOR score of 59.98 and a BLEU-4 score of 32.06, respectively. Furthermore, over 64% of the generated captions received human ratings above 3.5 on a 7-point scale, with RLHF yielding a 13% improvement in the overall rating distribution, indicating stronger alignment with human haptic perception. These findings highlight the potential of large language models to process and adapt to sensory data.

HapticLLaMA: A Multimodal Sensory Language Model for Haptic Captioning

While large language models (LLMs) have achieved remarkable success in providing trustworthy responses for knowledge-intensive tasks, they still face critical limitations such as hallucinations and outdated knowledge. To address these issues, the retrieval-augmented generation (RAG) framework enhances LLMs with access to external knowledge via a retriever, enabling more accurate and real-time outputs about the latest events. However, this integration brings new security vulnerabilities: the risk that malicious content in the external database can be retrieved and used to manipulate model outputs. Although prior work has explored attacks on RAG systems, existing approaches either rely heavily on access to the retriever or fail to jointly consider both retrieval and generation stages, limiting their effectiveness, particularly in black-box scenarios. To overcome these limitations, we propose Token-level Precise Attack on the RAG (TPARAG), a novel framework that targets both white-box and black-box RAG systems. TPARAG leverages a lightweight white-box LLM as an attacker to generate and iteratively optimize malicious passages at the token level, ensuring both retrievability and high attack success in generation. Extensive experiments on open-domain QA datasets demonstrate that TPARAG consistently outperforms previous approaches in retrieval-stage and end-to-end attack effectiveness. These results further reveal critical vulnerabilities in RAG pipelines and offer new insights into improving their robustness.

Token-Level Precise Attack on RAG: Searching for the Best Alternatives to Mislead Generation

Emotion recognition in conversation (ERC) requires understanding both contextual dependencies and speaker-specific cues. Existing approaches often treat conversation context as a single representation or encode speaker identity shallowly, limiting their ability to capture fine-grained emotional dynamics. We propose PERC, a personality-aware ERC framework that (1) segregates conversational context into intra- and inter-speaker components, (2) models static or dynamic personality traits to represent stable and evolving speaker dispositions, and (3) performs contrastive cross-alignment between intra-intra and inter-inter representations to enforce contextual and personality consistency. Experiments on three ERC benchmarks show that PERC achieves new state-of-the-art performance, improving weighted F1 by up to 2.74%, demonstrating the effectiveness of integrating context segregation, personality modeling, and contrastive alignment for emotion reasoning in dialogue.

Who You Are, What You Say: Intra- and Inter- Context Personality for Emotion Recognition in Conversation

Knowledge Distillation (KD) has emerged as a crucial technique for compressing Large Language Models (LLMs). Although existing cross-tokenizer KD methods have made notable progress, their effectiveness remains constrained by suboptimal alignment across sequence and vocabulary levels. To address these limitations, we introduce Dual-Space Weighting and Time-Warped Alignment (DWA-KD), a novel cross-tokenizer distillation framework that enhances token-wise distillation through dual-space entropy-based weighting and achieves precise sequence-level alignment by leveraging both lexical and semantic information. At the token level, DWA-KD maps teacher representations into the student space and vice versa, performing dual-space KD via KullbackвЂ“Leibler divergence (KL). The process is modulated by dual-space entropy-based weights that up-weight tokens where the student is uncertain and the teacher is confident, thereby focusing learning on informative tokens rather than treating all positions equally. At the sequence level, DWA-KD applies Soft Dynamic Time Warping (Soft-DTW) to both the embedding and final hidden-state layers, enabling robust alignment of lexical and contextual semantics between teacher and student sequences. Extensive experiments across diverse NLP benchmarks demonstrate that DWA-KD consistently outperforms state-of-the-art KD baselines, while ablation studies confirm the complementary contributions of entropy-based token weighting and embedding and final hidden state layer Soft-DTW alignment.

DWA-KD: Dual-Space Weighting and Time-Warped Alignment for Cross-Tokenizer Knowledge Distillation

In the tabular domain, which is the predominant data format in real-world applications, anomalies are extremely rare or difficult to collect, as their identification often requires domain expertise. Consequently, evaluating tabular anomaly detection models is challenging, since anomalies may be absent even in evaluation sets. To tackle this challenge, prior works have generated synthetic anomaly generation rely on statistical patterns, they often overlook domain semantics and struggle to reflect the complex, domain-specific nature of real-world anomalies. We propose AutoAnoEval, a novel evaluation framework for tabular AD that constructs pseudo-evaluation sets with semantically grounded synthetic anomalies. Our approach leverages an iterative interaction between a Large Language Model (LLM) and a decision tree (DT): the LLM generates realistic anomaly conditions based on contextual semantics, while the DT provides structural guidance by capturing feature interactions inherent in the tabular data. This iterative loop ensures the generation of diverse anomaly conditions, ranging from easily detectable outliers to subtle cases near the decision boundary. Extensive experiments on 20 tabular AD benchmarks demonstrate that AutoAnoEval achieves superior model selection performance, with high ranking alignment and minimal performance gaps compared to evaluations on anomalies encountered in practical applications.

AutoAnoEval: Semantic-Aware Model Selection via Tree-Guided LLM Reasoning for Tabular Anomaly Detection

Dynamic topic models aim to reveal how themes emerge, evolve, and dissolve in time-stamped corpora, but existing approaches still face three major challenges: (i) encoders capture bag-of-words statistics but fail to align with the rich semantic priors of large pre-trained language models, (ii) temporal linkages are often modeled as rigid one-to-one chains, limiting the ability to track non-linear evolution such as topic splits or merges, and (iii) interpretability remains shallow, relying on noisy top-word lists that obscure thematic clarity. We propose L-DNTM (LLM-Augmented for Dynamic Neural Topic Model), a variational framework designed to capture more faithful temporal trajectories. Our model integrates three key components: multi-objective distillation to inject PLM-derived semantic knowledge into the encoder, entropy-regularized optimal transport to align entire topic constellations across time for smooth yet flexible evolution, and LLM-guided refinement to sharpen topicвЂ“word distributions for improved interpretability. Extensive experiments on diverse corpora show that L-DNTM yields more coherent, temporally consistent, and interpretable topic dynamics, and further enhances downstream classification and clustering tasks.

Beyond Coherence: Improving Temporal Consistency and Interpretability in Dynamic Topic Models

Large language models (LLMs) have shown strong performance in zero-shot summarization, but often struggle to model document structure and identify salient information in long texts. In this work, we introduce StrucSum, a training-free prompting framework that enhances LLM reasoning through sentence-level graph structures. StrucSum injects structural signals into prompts via three targeted strategies: Neighbor-Aware Prompting (NAP) for local context, Centrality-Aware Prompting (CAP) for importance estimation, and Centrality-Guided Masking (CGM) for efficient input reduction. Experiments on ArXiv, PubMed, and Multi-News demonstrate that StrucSum consistently improves both summary quality and factual consistency over unsupervised baselines and vanilla prompting. In particular, on ArXiv, it increases FactCC and SummaC by 19.2% and 8.0% points, demonstrating stronger alignment between summaries and source content. The ablation study shows that the combination of multiple strategies does not yield clear performance gains; therefore, structure-aware prompting with graph-based information represents a promising and underexplored direction for the advancement of zero-shot extractive summarization with LLMs.

StrucSum: Graph-Structured Reasoning for Long Document Extractive Summarization with LLMs

Understanding regional similarities is crucial for applications such as urban planning, tourism recommendations, business expansion, and disease prevention. While spatial data, including POI distributions, check-in activity, and building footprints, offer valuable insights, existing similarity methodsвЂ”based on distance metrics, embeddings, or deep metric learningвЂ”fail to capture the contextual richness and adapt to heterogeneous spatial data. To overcome these limitations, we introduce a novel similar region search framework that ranks candidate regions based on their similarity to a query region using large language models. To further enhance performance, we fine-tune the model through self-supervised learning by introducing controlled noise into spatial data. This generates similar and dissimilar samples without relying on extensive labeled data. By transforming spatial data into natural language descriptions, our method seamlessly integrates heterogeneous datasets without requiring structural modifications, ensuring scalability across diverse urban contexts. Experiments on real-world datasets demonstrate the effectiveness of our framework, showing significant improvements in accuracy and ranking performance compared to state-of-the-art methods.

Similar Region Search using LLMs on Spatial Feature Space

Emotion recognition in multi-speaker conversations faces significant challenges due to speaker ambiguity and severe class imbalance. We propose a novel framework that addresses these issues through three key innovations: (1) a speaker identification module that leverages audio-visual synchronization to accurately identify the active speaker, (2) a knowledge distillation strategy that transfers superior textual emotion understanding to audio and visual modalities, and (3) hierarchical attention fusion with composite loss functions to handle class imbalance. Comprehensive evaluations on MELD and IEMOCAP datasets demonstrate superior performance, achieving 67.75% and 72.44% weighted F1 scores respectively, with particularly notable improvements on minority emotion classes.

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