Morocco

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.

EACL 2026

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

multimodal reasoning

figurative language

multimodality

### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Welcome to the 2025 meeting of the Nations of the Americas Chapter of the Association for Computational Linguistics! I am proud to help organize the first NAACL conference to carry the new name of our organization, one that emphasizes inclusion for all of the Americas. I am also pleased to welcome you to Albuquerque, New Mexico, a state whose unique blend of cultural influences will make for an excellent backdrop for NAACL 2025, especially with this year’s special theme on NLP in a Multicultural World. 
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NAACL 2025

Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

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.
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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. 

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.

Quantifying Data Contamination in Psychometric Evaluations of LLMs

Knowledge Graph-enhanced Large Language Models (KG-Enhanced LLMs) integrate the linguistic capabilities of LLMs with the structured semantics of Knowledge Graphs (KGs), showing strong potential in knowledge-intensive reasoning tasks. However, existing methods typically adopt query-driven iterative reasoning from a local perspective, which limits their ability to capture semantically distant but crucial information, leading to dual bottlenecks in efficiency and accuracy for complex multi-hop tasks. To address this issue, we propose MIAoG, a multi-view instructed adaptive reasoning of LLM on KG, which is designed to overcome the limitations of local exploration by enabling LLMs to plan, evaluate, and adapt reasoning paths from a global perspective. Instead of query-anchored exploration, MIAoG first prompts the LLM to generate a multi-view instruction set that outlines diverse potential reasoning paths and explicitly specifies global reasoning intentions to guide the model toward coherent and targeted reasoning. During reasoning, MIAoG integrates a real-time introspection mechanism that evaluates the alignment between the current path and the instructions, adaptively pruning inconsistent trajectories to enhance global consistency while maintaining efficiency. Extensive experiments on multiple public datasets show that MIAoG achieves state-of-the-art performance in KG-enhanced LLM reasoning, particularly excelling in complex multi-hop scenarios.

Thinking Beyond the Local: Multi-View Instructed Adaptive Reasoning in KG-Enhanced LLMs

Time series data is ubiquitous across various domains, including manufacturing, finance, and healthcare. High-quality annotations are essential for effectively understanding time series and facilitating downstream tasks. However, obtaining such annotations is challenging, particularly in mission-critical domains. In this paper, we propose TESSA, a multi-agent system designed to automatically generate both general and domain-specific annotations for time series data. TESSA introduces two agents: a general annotation agent and a domain-specific annotation agent. The general agent captures common patterns and knowledge across multiple source domains, leveraging both time-series-wise and text-wise features to generate general annotations. Meanwhile, the domain-specific agent utilizes limited annotations from the target domain to learn domain-specific terminology and generate targeted annotations. Extensive experiments on multiple synthetic and real-world datasets demonstrate that TESSA effectively generates high-quality annotations, outperforming existing methods. Our code and data are available at https://anonymous.4open.science/r/TESSA-8B7D.

Decoding Time Series with LLMs: A Multi-Agent Framework for Cross-Domain Annotation

Hallucinations generated by Large Language Models (LLMs) pose significant challenges for their application to low-resource languages. We present Multi-Hall-SA, a cross-lingual benchmark for hallucination detection spanning English and four low-resource South African languages: isiZulu, isiXhosa, Sepedi, and Sesotho. Derived from government texts, this benchmark categorizes hallucinations into four types aligned with established taxonomies of factual errors: temporal shifts, entity errors, numerical inaccuracies, and location mistakes. Human validation confirms the quality and cross-lingual alignment of our synthetically generated hallucinations. Our cross-lingual alignment methodology enables direct performance comparison between high-resource and low-resource languages, revealing notable gaps in detection capabilities. Evaluation across four state-of-the-art models shows they detect up to 23.6% fewer hallucinations in South African languages compared to English. Knowledge augmentation reduces this disparity, decreasing cross-lingual performance gaps by 59.4% on average. Beyond introducing a validated resource for low-resource languages, Multi-Hall-SA provides a framework for evaluating and improving factual reliability across linguistic boundaries, advancing more inclusive and equitable AI development.

Multi-Hall-SA: A Cross-lingual Benchmark for Multi-Type Hallucination Detection in Low-Resource South African Languages

We present **MMUIE**, a large-scale universal dataset for multi-domain, document-level information extraction (IE) from long texts. Existing IE systems predominantly operate at the sentence level or within narrow domains due to annotation constraints. MMUIE addresses this gap by introducing an automated annotation pipeline that integrates traditional knowledge bases with large language models to extract fine-grained entities, aliases, and relation triples across 34 domains. The dataset comprises a weakly-supervised training set and a manually verified test set, featuring 723 entity types and 456 relation types. Empirical evaluations reveal that existing sentence-level IE models and even advanced LLMs underperform on this task, highlighting the need for better domain-aware document-level models. To this end, we develop DocUIE, a universal IE model fine-tuned on MMUIE, which achieves strong generalization and transferability across domains. MMUIE lays the foundation for robust, scalable, and universal information extraction from long-form text in diverse real-world scenarios. All code and data will be made publicly available.

MMUIE: Massive Multi-Domain Universal Information Extraction for Long Documents

Research on developmentally plausible language models has so far centered on English, leaving open questions about multilingual settings. We present a systematic study of compact models by extending BabyBERTa to EnglishвЂ“French scenarios under strictly size-matched data conditions, addressing {\it monolingual}, {\it bilingual}, and {\it cross-lingual} settings. Our design contrasts two corpus types: (i) child-directed speech (2.5M tokens), following BabyBERTa and related work, and (ii) multi-domain corpora (10M tokens), extending the BabyLM framework to French. To support fair evaluation, we also introduce new resources: French versions of QAMR and QASRL, and an English and French multi-domain corpus. We evaluate the models on both syntax and semantics tasks, comparing with Wikipedia-only training. Results reveal context-dependent effects. Training on Wikipedia consistently favors semantic tasks, while child-directed speech improves grammatical judgments in monolingual settings. Bilingual pretraining yields notable gains for Textual Entailment, with French benefiting from curriculum learning. Importantly, the same relative patterns emerge across BabyBERTa, RoBERTa, and LTG-BERT, suggesting architecture-independent findings.

Learning from Child-directed Speech in Two-language Scenarios: A French-English Case-Study

Recent attempts to leverage large language models (LLMs) for reasoning and pre-trained knowledge in multi-modal reasoning focus on two main approaches: aligning image features with linguistic space, and converting images into textual cues to exploit the implicit reasoning capabilities of LLMs. Although they integrate visual information into the reasoning pipeline, they often treat visual perception and language reasoning as separate processes, limiting the potential for fully unified multi-modal reasoning. In this paper, we propose a novel method, VisualвЂ“Linguistic Abductive Reasoning (ViLA), inspired by human abductive reasoning processes. ViLA hypothesizes a plausible answer, generates the corresponding visual and textual premises, and employs fuzzy scoring to select the most coherent combination, thus deriving the final inference. This process integrates visual and linguistic modalities into interpretable abductive reasoning chains, enabling unified multi-modal reasoning. Without fine-tuning LLMs or retrieving external knowledge, ViLA improves performance by 2.31% on AOKVQA, 1.7% on OKVQA, and 1.7% on GQA over previous state-of-the-art models, while also improving interpretability and stability.

Visual–Linguistic Abductive Reasoning with LLMs for Knowledge-based Visual Question Answering

Training complexity often scales with the size of hyperparameter space for Large Language Models (LLMs). While Direct Preference Optimization (DPO) offers learning stability through reparameterizing the reward function, its regularization against the reference policy can lead to suboptimal outcomes when the reference policy is not optimal. Recent DPO variants address this concern, but at a cost: they introduce additional hyperparameters, reducing feasibility for LLM fine-tuning. To overcome this challenge, we introduce Implicit policy Regularized Preference Optimization (IRPO), which tackles suboptimality while maintaining training simplicity. By treating the winning policy that generated the chosen responses in a pairwise dataset as an implicit policy, IRPO maximizes KL-regularized reward without extra hyperparameters. Then we propose a novel PO algorithm that directly optimizes the IRPO objective by estimating the likelihood ratio between implicit policies. As the winning policy generally outperforms the reference policy, IRPO can effectively address suboptimality. Our experiments show that IRPO significantly outperforms baseline algorithms with the same hyperparameter complexity. Moreover, IRPO demonstrates comparable performance to recent algorithms that rely on a larger number of hyperparameters, offering a practical solution for scalable LLM fine-tuning.

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

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