Morocco

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.

EACL 2026

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

graph-structured reasoning

large language models

extractive summarization

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. 

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.

Emotion Recognition in Multi-Speaker Conversations through Speaker Identification, Knowledge Distillation, and Hierarchical Fusion

Human communication is often implicit, conveying tone, identity, and intent beyond literal meanings. While large language models have achieved strong performance on explicit tasks such as summarization and reasoning, their capacity for expressivity, or implicit communication, remains underexplored. We introduce ExpressivityBench, a framework for evaluating the expressivity of LLMs using information-theoretic communication models. Our approach quantifies how well LLM-generated text communicates target properties without explicit mention, across nine tasks spanning emotion, identity, and tone. To enable scalable and reproducible evaluation, we employ LLM-based graders validated against human judgments. Our results reveal that while models are adept at expressing affective content, they struggle with sociolinguistic signals, lagging behind human baselines. This study provides a necessary step to evaluate human-like implicit communication, with implications for applications such as education, mental health support, and socially-aware dialogue systems. We provide code and data for our benchmark alongside our paper.

ExpressivityBench: Can LLMs Communicate Implicitly?

Large Language Model (LLM)вЂ“based Multi-Agent Systems (MAS) have emerged as a powerful paradigm for tackling complex, multi-step tasks across diverse domains. However, despite their impressive capabilities, MAS remain susceptible to adversarial manipulation. Existing studies typically examine isolated attack surfaces or specific scenarios, leaving a lack of holistic understanding of MAS vulnerabilities. To bridge this gap, we introduce PEAR, a benchmark for systematically evaluating both the utility and vulnerability of plannerвЂ“executor MAS. While compatible with various MAS architectures, our benchmark focuses on the plannerвЂ“executor structureвЂ”a practical and widely adopted design. Through extensive experiments, we find that (1) a weak planner degrades overall clean task performance more severely than a weak executor; (2) while a memory module is essential for the planner, having a memory module for the executor does not impact the clean task performance; (3) there exists a trade-off between task performance and robustness; and (4) attacks targeting the planner are particularly effective at misleading the system. These findings offer actionable insights for enhancing the robustness of MAS and lay the groundwork for principled defenses in multi-agent settings.

PEAR: Planner-Executor Agent Robustness Benchmark

Text-to-Image models may generate harmful content, such as pornographic images, particularly when unsafe prompts are submitted. To address this issue, safety filters are often added on top of text-to-image models, or the models themselves are aligned to reduce harmful outputs. However, these defenses remain vulnerable when an attacker strategically designs adversarial prompts to bypass these safety guardrails. In this work, we propose PromptTune, a method to jailbreak text-to-image models with safety guardrails using a fine-tuned large language model. Unlike other query-based jailbreak attacks that require repeated queries to the target model, our attack generates adversarial prompts efficiently after fine-tuning our AttackLLM. We evaluate our method on three datasets of unsafe prompts and against five safety guardrails. Our results demonstrate that our approach effectively bypasses safety guardrails, outperforms existing no-box attacks, and also facilitates other query-based attacks.

Jailbreaking Safeguarded Text-to-Image Models via Large Language Models

Speculative decoding has emerged as an effective method to reduce latency and inference cost of LLM inferences. However, there has been inadequate attention towards the energy requirements of these models. To address this gap, this paper presents a comprehensive survey of energy requirements of speculative decoding strategies, with detailed analysis on how various factors -- model size and family, speculative decoding strategies, and dataset characteristics -- influence the energy optimizations.

Benchmarking the Energy Savings with Speculative Decoding Strategies

Contrastive learning (CL) has achieved remarkable progress in natural language processing (NLP), primarily as a paradigm for pre-training and fine-tuning. However, its potential during the generation phase, particularly in in-context learning (ICL)-based retrieval-augmented summarization, remains largely unexplored. While previous studies have attempted to incorporate negative samples into ICL prompts, these methods do not enforce a true contrastive objective that encourages separation of positive and negative samples in the representation space. In this paper, we first demonstrate through preliminary experiments that small language models (SLMs) can interpret contrastive prompts and effectively distinguish between positive and negative samples during inference, without any parameter updates. Building on these findings, we propose ConRAS, a novel framework that injects contrastive objectives into ICL-based retrieval-augmented summarization. Extensive experiments and in-depth analysis on three summarization benchmarks using four SLMs show that ConRAS consistently outperforms state-of-the-art retrieval-augmented methods, achieving significant improvements in summary quality.

ConRAS: Contrastive In-context Learning Framework for Retrieval-Augmented Summarization

Safety-critical classification tasks face a persistent challenge: traditional models achieve high overall accuracy but inadequate performance on critical minority classes. We introduce a numbers to narratives framework that transforms tabular data into contextually rich descriptions, enabling language models to leverage pre-trained knowledge for minority class detection. Our approach integrates structured verbalization, linguistically-informed augmentation, and parameter-efficient fine-tuning to address the "minority class blind spot'' in high-consequence domains. Using a significantly more efficient model architecture than existing approaches, our framework achieves superior minority class F1-scores: 78.76% for machine failures (+7.42 points over XGBoost), 65.87% for at-risk students (+12.12 points over MLP), and 32.00% for semiconductor failures (+1.01 points over XGBoost, despite 14:1 class imbalance). Our approach also improves overall accuracy by up to 22.43% in five of six datasets while maintaining computational feasibility. Ablation studies confirm that narrative-based verbalization enables effective reasoning about tabular data by contextualizing abstract numerical features. This work provides a practical, resource-efficient approach for enhancing minority class performance in safety-critical domains.

From Numbers to Narratives: Efficient Language Model-Based Detection for Safety-Critical Minority Classes

Recent reinforcement learning (RL)-trained language models have demonstrated strong performance on complex reasoning tasks by producing long and detailed reasoning traces. However, despite these advancements, they often struggle with finding the right balance in reasoning length: some terminate prematurely before reaching a correct answer (underthinking), while others continue reasoning beyond necessity, leading to inefficiency or even degraded accuracy (overthinking). To address these challenges, we propose a method for optimizing reasoning length via self-assessed confidence. By prompting the model to evaluate its own confidence at intermediate reasoning steps, we enable dynamic stopping once sufficient reasoning is achieved. Experiments across multiple reasoning benchmarks show that our approach improves computational efficiency without compromising answer quality. Furthermore, we find that confidence estimates from RL-trained reasoning models are more reliable than those from standard LLMs, making it a valuable internal signal for controlling reasoning depth.

Think Just Enough: Leveraging Self-Assessed Confidence for Adaptive Reasoning in Language Models

Large language models demonstrate limited capability in proficiency-controlled sentence simplification, particularly when simplifying across large readability levels. We propose a framework that decomposes complex simplifications into manageable steps through dynamic path planning, semantic-aware exemplar selection, and chain-of-thought generation with conversation history for coherent reasoning. Evaluation on five languages across two benchmarks shows our approach improves simplification effectiveness while reducing computational steps. Human evaluation confirms the fundamental trade-off between simplification effectiveness and meaning preservation. Notably, even human annotators struggle to agree on semantic preservation judgments, highlighting the inherent complexity of this task. Our work shows that while step-by-step simplification improves control, preserving semantic fidelity during extensive simplification remains an open challenge.

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