Morocco

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.

EACL 2026

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

retrieval-augmented generation

contrastive learning

summarization

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible. The experience for on-site participants would closely approximate a normal pre-COVID *ACL conference, with 5-6 thematically organized parallel sessions and live Q/A and interactive discussion immediately after the talks. Presentations by virtual participants will be equitably interleaved with those of on-site participants, projected on the auditorium screens as if on-site, and also followed immediately by live Q/A and interactive discussion at a time during reasonable waking hours for the virtual presenter. For all participants, on-site and virtual, who are unable to attend a session due to either time-zone issues or because they are participating in another session live, talk recordings and slides will be available online at a minimum after the live presentation (and in many cases before as well), and questions may be submitted in advance on session-specific discussion boards and answered live in session with the usual visual aids if desired.

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EMNLP 2021

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

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. 

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.

Let's Simplify Step by Step: Guiding LLM Towards Multilingual Unsupervised Proficiency-Controlled Sentence Simplification

Manga, or Japanese comics, is a richly multimodal narrative form that blends images and text in complex ways. Teaching large multimodal models (LMMs) to understand such narratives at a human-like level could help manga creators reflect on and refine their stories. To this end, we introduce two benchmarks for multimodal manga understanding: MangaOCR, which targets in-page text recognition, and MangaVQA, a novel benchmark designed to evaluate contextual understanding through visual question answering. MangaVQA consists of 526 high-quality, manually constructed question-answer pairs, enabling reliable evaluation across diverse narrative and visual scenarios. Building on these benchmarks, we develop MangaLMM, a manga-specialized model finetuned from the open-source LMM Qwen2.5-VL to jointly handle both tasks. Through extensive experiments, including comparisons with proprietary models such as GPT-4o and Gemini 2.5, we assess how well LMMs understand manga. Our benchmark and model provide a comprehensive foundation for evaluating and advancing LMMs in the richly narrative domain of manga.

MangaVQA and MangaLMM: A Benchmark and Specialized Model for Multimodal Manga Understanding

Understanding user intent in online reviews requires modeling not only explicit aspect ratings but also implicit motivations shaped by contextual factors. Existing large language models (LLMs) often lack structured grounding, fail to capture nuanced intent expression. We propose HII-KG, a two-stage Hierarchical Intent Inference framework that first predicts fine-grained aspect ratings and then generates natural language intent statements, guided by contextual subgraphs retrieved from a domain-specific knowledge graph (KG). We first employ parameter-efficient fine-tuning of LLaMA3.1-8B to predict aspect ratings in an instruction-based format. Moreover, we leverage Cypher-aware prompting to generate user intent from KG summaries. Experiments on a online hotel review dataset show that HII-KG consistently outperforms strong LLM and encoder-based baselines in both aspect classification (avg. F1 +4.5%) and intent generation (BLEU +3.3, ROUGE-L +2.9). The results demonstrate that structured KG integration can significantly enhance fluency, contextual relevance, and factual alignment in user intent inference.

Hierarchical User Intent Inference with Knowledge Graph Grounding

Text embedding models are widely used in natural language processing applications. However, their capability is often benchmarked on tasks that do not require understanding nuanced numerical information in text. As a result, it remains unclear whether current embedding models can precisely encode numerical content, such as numbers, into embeddings. This question is critical because embedding models are increasingly applied in domains where numbers matter, such as finance and healthcare. For example, ''Company X's market share grew by 2\%'' should be interpreted very differently from ''Company X's market share grew by 20%'' , even though both indicate growth in market share. This study aims to examine whether text embedding models can capture such nuances. Using synthetic data in a financial context, we evaluate 13 widely used text embedding models and find that they generally struggle to capture numerical details accurately. Our further analyses provide deeper insights into embedding numeracy, informing future research to strengthen embedding model-based NLP systems with improved capacity for handling numerical content.

Revealing the Numeracy Gap: An Empirical Investigation of Text Embedding Models

VisionвЂ“language models (VLMs) have demonstrated strong reasoning abilities in literal multimodal tasks such as visual mathematics and science question answering. However, figurative languageвЂ”such as sarcasm, humor, and metaphorвЂ”remains a significant challenge, as it conveys intent and emotion through subtle incongruities between expressed and intended meanings. In multimodal settings, accompanying images can amplify or invert textual meaning, demanding models that reason across modalities and account for subjectivity. We propose a three-step framework for developing efficient multimodal reasoning models that can (i) interpret multimodal figurative language, (ii) provide transparent reasoning traces, and (iii) generalize across multiple figurative styles. Experiments across four styles show that (1) incorporating reasoning traces substantially improves multimodal figurative understanding, (2) reasoning learned in one style can transfer to othersвЂ”especially between related styles like sarcasm and humor, and (3) training jointly across styles yields a generalized reasoning VLM that outperforms much larger open- and closed-source models. Our findings show that lightweight VLMs with verifiable reasoning achieve robust cross-style generalization and interpretable multimodal understanding.

Reasoning Beyond Literal: Cross-style Multimodal Reasoning for Figurative Language Understanding

Test-time compute has emerged as a promising paradigm that enables small language models (SLMs) to achieve large language model (LLM)-level capabilities by allocating additional compute for explicit reasoning during inference. Two common approaches are beam search and Best-of-N sampling. Beam search improves reasoning quality by scoring and optimizing token sequences using Process Reward Models (PRMs), but can incur non-trivial computational overhead and latency. In contrast, Best-of-N executes all reasoning trajectories without PRM guidance, often wasting compute on low-quality trajectories that may have gone astray early in the generation process. To address both inefficiencies, we propose THROW (THink haRd Only When needed)—a hybrid inference pipeline that combines the diversity of Best-of-N with the reasoning trajectory optimization of beam search. THROW introduces a selective branch truncation and expansion mechanism: it generates shorter initial trajectories than Best-of-N and evaluates them using PRMs to classify each query as "easy" or "hard." Based on this classification, THROW applies branch truncation for easy queries, mimicking Best-of-N, and PRM-guided branch expansion for hard ones, similar to beam search. Evaluations on MATH500, AMC23, and AIME24 demonstrate that THROW achieves 1.54× and 14.38× latency speedups and 35.7% and 80.4% token reductions on average while preserving high reasoning accuracy compared to Best-of-N and Beam Search, respectively.

Think Hard Only When Needed: A Hybrid Best-of-N and Beam Search for Efficient Test-Time Compute

Automated reasoning is critical in domains such as law and governance, where verifying claims against facts in documents requires both accuracy and interpretability. Recent work has adopted a structured reasoning paradigm that parses first-order logic (FOL) rules from natural language and delegates inference to automated solvers. With the rise of large language models (LLMs), methods such as GCD and CODE4LOGIC leverage their reasoning and code generation capabilities to enhance logic parsing. However, these approaches suffer from (1) fragile syntax control, due to weak enforcement of global grammar consistency, and (2) low semantic faithfulness, as they lack fine-grained clause-level semantic understanding. To address these challenges, we propose NL2Logic, a framework that uses the Abstract Syntax Tree (AST) as an intermediate reasoning layer to translate natural language into logic expressions with both semantic fidelity and syntactic precision. It consists of a recursive LLM-based semantic parser that constructs a precise AST capturing clause-level semantics, and an AST-guided generator that deterministically compiles the parsed logic into solver-ready code through a two-pass algorithm. On the FOLIO and LogicNLI benchmarks, NL2Logic attains 99% syntactic accuracy and improves semantic correctness by 30% over state-of-the-art baselines, establishing a promising pathway for verifiable natural language reasoning.

NL2Logic: AST-Guided Translation of Natural Language into First-Order Logic with Large Language Models

Recent studies apply psychometric questionnaires to Large Language Models (LLMs) to assess high-level psychological constructs such as values, personality, moral foundations, and dark traits. Although prior work has raised concerns about possible data contamination from psychometric inventories, which may threaten the reliability of such evaluations, there has been no systematic attempt to quantify the extent of this contamination. To address this gap, we propose a framework to systematically measure data contamination in psychometric evaluations of LLMs, evaluating three aspects: (1) item memorization, (2) evaluation memorization, and (3) target score matching. Applying this framework to 21 models from major families and four widely used psychometric inventories, we provide evidence that popular inventories such as the Big Five Inventory (BFI-44) and Portrait Values Questionnaire (PVQ-40) exhibit strong contamination, where models not only memorize items but can also adjust their responses to achieve specific target scores.

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

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Constructing contrastive samples via summarization for text classification with limited annotations

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