Morocco

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.

EACL 2026

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

vision question answering

knowledge base qa

reasoning

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ACL-IJCNLP 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

technical paper

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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. 

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.

IRPO: Implicit Policy Regularized Preference Optimization

The rise of large language models (LLMs) has enabled us to seek answers to inherently debatable questions on LLM chatbots, necessitating a reliable way to evaluate their ability. However, traditional QA benchmarks assume fixed answers are inadequate for this purpose. To address this, we introduce DebateQA, a dataset of 2,941 debatable questions, each accompanied by multiple human-annotated partial answers that capture a variety of perspectives. We develop two metrics: Perspective Diversity, which evaluates the comprehensiveness of perspectives, and Dispute Awareness, which assesses if the LLM acknowledges the question's debatable nature. Experiments demonstrate that both metrics are aligned with human preferences and stable across different underlying models. Using DebateQA with two metrics, we assess 12 prevalent LLMs and retrieval-augmented generation methods. Our findings reveal that while LLMs generally excel at recognizing debatable issues, their ability to provide comprehensive answers encompassing diverse perspectives varies considerably.

DebateQA: Evaluating Question Answering on Debatable Knowledge

The advancement of reasoning capabilities in Large Language Models (LLMs) requires substantial amounts of high-quality reasoning data, particularly in mathematics. Existing data synthesis methods, such as data augmentation from annotated training sets or direct question generation based on relevant knowledge points and documents, have expanded datasets but face challenges in mastering the internal logic of the problem during generation and ensuring the verifiability of the solutions. To address these issues, we propose \textbf{RV-Syn}, a novel \underline{\textbf{R}}ational and \underline{\textbf{V}}erifiable mathematical \underline{\textbf{Syn}}thesis approach. RV-Syn first constructs a structured library of mathematical operations and then composes them into executable computational graphs, which serve as verifiable solution blueprints. These graphs are subsequently back-translated into complex problems, enabling solution-guided, logic-aware problem generation while inherently ensuring the verifiability of the solving process. Experimental results show RV-Syn surpasses existing synthesis methods, including those involving human-crafted problems. Our method achieves a 6.3% performance gain over the previous state-of-the-art synthetic data on LLaMA-3-8B and demonstrates superior data efficiency, outperforming others with only half the training data (50k vs. 100k), enabling a more scalable and robust reasoning dataset generation framework.

RV-Syn: Rational and Verifiable Mathematical Reasoning Data Synthesis based on Structured Function Library

Reasoning language models have set state-of-the-art (SOTA) records on many challenging benchmarks, enabled by multi-step reasoning induced by reinforcement learning. However, reasoning models are prone to generating confident, plausible responses that are incorrect (hallucinations). Knowing when and how much to trust these models is critical for safe deployment in real-world applications. To this end, we explore uncertainty quantification (UQ) of reasoning models in this work. We ask three fundamental questions: First, are reasoning models well-calibrated? Second, does deeper reasoning improve model calibration? Finally, inspired by humans' innate ability to double-check their thought processes to verify the validity of their answers and their confidence, we ask: can reasoning models improve their calibration by explicitly reasoning about their chain-of-thought traces? We introduce introspective uncertainty quantification (IUQ) to explore this direction. In extensive evaluations on SOTA reasoning models across a broad range of benchmarks, we find that reasoning models: (i) are typically overconfident, (ii) become even more overconfident with deeper reasoning, and (iii) can become better calibrated through introspection (e.g., o3-Mini and DeepSeek R1) but not uniformly (e.g., Claude 3.7 Sonnet becomes more poorly calibrated). We conclude with important research directions to design necessary UQ benchmarks and improve the calibration of reasoning models.

Reasoning about Uncertainty: Do Reasoning Models Know When They Don't Know?

Multimodal reasoning tasks such as visual question answering (VQA) require models to process both language and visual inputs. However, existing approaches typically decompose only language queries, treating images as monolithic inputs. We introduce REDI, a framework that jointly decomposes both images and questions into visual sub-domains (segmentation, material, depth, and color) with corresponding sub-questions. REDI employs an orchestrator agent that coordinates specialized worker agents, where each worker specializes in a specific visual sub-domain, and aggregates their responses through meta-reasoning. This hierarchical multi-agent design mitigates error propagation and improves compositional reasoning across both open- and closed-source MLLMs. On SEEDBench, MMBench, and CLEVR, REDI achieves absolute accuracy improvements of 8.9%, 8.2%, and 16.0%, respectively, marking a significant advancement in compositional multimodal reasoning. The code will be released on acceptance.

Compositional Reasoning via Joint Image and Language Decomposition

Identifying legal statutes given a situation is one of the most fundamental tasks in Legal NLP, and traditionally has been modeled using facts from court judgments as input, due to their abundance. However, in practical settings, the input queries are likely to be informal and asked by laymen, or non-professionals. This has led to the development of some layman LSI datasets, although there has been little research to explore the differences between court and layman data for LSI. In this work, we create ILSIC, a corpus of layman queries covering 500+ statutes from Indian law. Additionally, the corpus also contains court cases to enable researchers to effectively compare between court and layman data for LSI. We conducted extensive experiments on our corpus, including benchmarking over the layman dataset using zero and few-shot inference, retrieval-augmented generation and supervised fine-tuning. When comparing the different types of data, we observe that models trained purely on court queries are ineffective during test time on layman queries, while transfer learning from court to layman data can be beneficial in certain scenarios. We also conducted fine-grained analyses of our results in terms of categories of queries and frequency of statutes.

ILSIC: Corpora for Identifying Indian Legal Statutes from Queries by Laymen

Large language models (LLMs) are increasingly used as evaluators for natural language generation, applying human-defined rubrics to assess system outputs. However, human rubrics are often static and misaligned with how models internally represent language quality. We introduce GER-Eval (Generating Evaluation Rubrics for Evaluation) to investigate whether LLMs can design and use their own evaluation rubrics. We evaluate the semantic coherence and scoring reliability of LLM-defined criteria and their alignment with human criteria. LLMs reliably generate interpretable and task-aware evaluation dimensions and apply them within models, but their scoring reliability degrades in factual and knowledge-intensive settings. Closed-source models such as GPT-4o achieve higher agreement and cross-model generalization than open-weight models such as Llama. Our findings position evaluation as a learned linguistic capability of LLMsвЂ”consistent within models but fragmented across themвЂ”and call for new methods that jointly model human and LLM evaluative language to improve reliability and interpretability.

Learning to Judge: LLMs Designing and Applying Evaluation Rubrics

Federated fine‑tuning of foundation models is impeded by the need to communicate billions of parameters. Low‑rank adaptation (LoRA) alleviates this by updating only compact adapter matrices. However, varying client device capabilities lead to different adapter ranks, causing rank heterogeneity that undermines aggregation, and existing reconciliation methods still incur bias or inefficiency. To address this challenge, we propose RB-LoRA, a principled rank‑balanced aggregation framework that decomposes each update into rank‑wise components and aligns them using analytically derived weights. Experiments on both language and vision models demonstrate consistent improvements under one and three rounds of communication in federated learning.

RB-LoRA: Rank-Balanced Aggregation for Low-Rank Adaptation with Federated Fine-Tuning

Safety alignment aims to prevent Large Language Models (LLMs) from responding to harmful queries. To strengthen safety protections, jailbreak methods are developed to simulate malicious attacks and uncover vulnerabilities. In this paper, we introduce HILL (Hiding Intention by Learning from LLMs), a novel jailbreak approach that systematically transforms imperative harmful requests into learning-style questions with only straightforward hypotheticality indicators. Further, we introduce new metrics to thoroughly evaluate the efficiency and harmfulness of jailbreak methods. Experiments on the AdvBench dataset across a wide range of models demonstrate HILL's strong generalizability. It achieves top attack success rates on the majority of models and across malicious categories while maintaining high efficiency with concise prompts. Results of various defense methods show the robustness of HILL, with most defenses having mediocre effects or even increasing the attack success rates. In addition, the assessment of defenses on our constructed safe prompts reveals inherent limitations of LLMs' safety mechanisms and flaws in the defense methods. This work exposes significant vulnerabilities of safety measures against learning-style elicitation, highlighting a critical challenge of fulfilling both helpfulness and safety alignments.

A Simple and Efficient Learning-Style Prompting for LLM Jailbreaking

Modern logical reasoning with LLMs primarily relies on employing complex interactive frameworks that decompose the reasoning process into subtasks solved through carefully designed prompts or requiring external resources (e.g., symbolic solvers) to exploit their strong logical structures. While interactive approaches introduce additional overhead, hybrid approaches depend on external components, which limit their scalability. A non-interactive, end-to-end framework enables reasoning to emerge within the model itself---improving generalization while preserving analyzability without any external resources. In this work, we introduce a non-interactive, end-to-end framework for logical reasoning. We show that introducing structural information into the few-shot prompt activates a subset of attention heads that patterns aligned with logical reasoning operators. Building on this insight, we propose Attention-aware Intervention (AAI), a method that reweights attention scores across selected heads identified by their logical patterns. AAI offers an efficient way to steer the model's reasoning toward leveraging prior knowledge through attention modulation. Extensive experiments show that AAI enhances logical reasoning performance on three well-known benchmarks, including ProofWriter, PrOntoQA, and Logical Deduction, across model architectures and sizes while introducing no additional computational overhead.

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