Morocco

Structured output from large language models (LLMs) has enhanced efficiency in processing generated information and is increasingly adopted in industrial applications. Prior studies have investigated the impact of structured output on LLMs&#39; generation quality, often presenting one-way findings. Some suggest that structured format enhances completeness and factual accuracy, while others argue that it restricts the reasoning capacity of LLMs and leads to reductions in standard evaluation metrics. Potential limitations of these assessments include restricted testing scenarios, weakly controlled comparative settings, and reliance on coarse metrics. In this work, we present a refined analysis using causal inference. Based on one assumed and two guaranteed constraints, we derive five potential causal structures characterizing the influence of structured output on LLMs&#39; generation: (1) collider without m-bias, (2) collider with m-bias, (3) single cause from instruction, (4) single cause from output format, and (5) independence. Across seven public and one developed reasoning tasks, we find that coarse metrics report positive, negative, or neutral effects of structured output on GPT-4o&#39;s generation. However, causal inference reveals no causal impact in 43 out of 48 scenarios. In the remaining 5, 3 involve multifaceted causal structures influenced by concrete instructions. Further experiments show that OpenAI-o3 are more resilient to output formats than general-purpose GPT-4o and GPT-4.1, highlighting an unaware advantage of reasoning models.

EACL 2026

Quantifying the Impact of Structured Output Format on Large Language Models through Causal Inference

output format

structured output

json

yaml

large language model

causal inference

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

Structured output from large language models (LLMs) has enhanced efficiency in processing generated information and is increasingly adopted in industrial applications. Prior studies have investigated the impact of structured output on LLMs' generation quality, often presenting one-way findings. Some suggest that structured format enhances completeness and factual accuracy, while others argue that it restricts the reasoning capacity of LLMs and leads to reductions in standard evaluation metrics. Potential limitations of these assessments include restricted testing scenarios, weakly controlled comparative settings, and reliance on coarse metrics. In this work, we present a refined analysis using causal inference. Based on one assumed and two guaranteed constraints, we derive five potential causal structures characterizing the influence of structured output on LLMs' generation: (1) collider without m-bias, (2) collider with m-bias, (3) single cause from instruction, (4) single cause from output format, and (5) independence. Across seven public and one developed reasoning tasks, we find that coarse metrics report positive, negative, or neutral effects of structured output on GPT-4o's generation. However, causal inference reveals no causal impact in 43 out of 48 scenarios. In the remaining 5, 3 involve multifaceted causal structures influenced by concrete instructions. Further experiments show that OpenAI-o3 are more resilient to output formats than general-purpose GPT-4o and GPT-4.1, highlighting an unaware advantage of reasoning models.

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. 

Zero-shot video captioning requires that a model generate high-quality captions without human-annotated video-text pairs for training. State-of-the-art approaches to the problem leverage CLIP to extract video-informed text prompts to guide language models in generating captions. However, by using representations at a single granularity (e.g., noun phrases or full sentences), these methods tend to focus on one key aspect of the scene and build a caption that ignores the rest of the visual input. To address this issue, and generate more accurate and complete captions, we propose a novel progressive multi-granularity textual prompting strategy for zero-shot video captioning. Our approach constructs three distinct memory banks, encompassing noun phrases, scene graphs of noun phrases, and entire sentences. Moreover, we introduce a category-aware retrieval mechanism that models the distribution of natural language surrounding the specific topics, to promote prompt diversity while ensuring visual relevance. Extensive experiments on both in-domain and cross-domain settings demonstrate that the proposed method consistently outperforms state-of-the-art approaches.

The Devil is in the Distributions: Explicit Modeling of Scene Content is Key in Zero-Shot Video Captioning

Reward models are crucial for aligning Large Language Models (LLMs) with human preferences but face two representative challenges. First, traditional discriminative reward models usually concatenate questions and responses directly as input, resulting in low data efficiency. Second, reward models are vulnerable to reward overoptimization. We propose \textbf{PIRA}, a training paradigm addressing these issues through three strategies: (1) Reformulating questionвЂ“answer pairs into preference-based instructions for clearer and more explicit task specification, (2) aggregating rewards from diverse preference tasks to reduce bias and improve robustness, and (3) averaging value-head outputs under varying dropout rates to stabilize rewards. Extensive experiments have demonstrated the effectiveness of PIRA.

PIRA: Preference-Oriented Instruction-Tuned Reward Models with Dual Aggregation

Large language models (LLMs) can exhibit political biases, which creates a risk of undue influence on LLM users and public opinion. Yet despite LLMs being used across the world, there is little evidence on how political biases vary across languages. And despite a growing number of frontier LLMs (e.g., DeepSeek) released by non-U.S. organizations, there is limited understanding of how political biases vary across LLMs developed in different political contexts. To address these gaps, we measure LLM bias on U.S.- and China-related issues, and how bias varies by 1) prompt language (English vs. Chinese) and 2) model origin (U.S. vs. Chinese). For this purpose, we create a new parallel dataset of 36k realistic test prompts asking models to write about a balanced set of 60 political issues sourced from national U.S. and Chinese news outlets. Using this dataset, we show that both model origin and prompt language systematically influence bias. Language effects dominate on China-related issues, particularly those involving sovereignty and human rights, while model origin effects are more pronounced on U.S.-related governance and foreign policy topics. Overall, our results highlight a need for language and context-specific measurement of LLM political bias.

Bias in the East, Bias in the West: A Bilingual Analysis of LLM Political Bias on U.S.- and China-Related Issues

Recent advancements in Large Language Models (LLMs) have shown promise for automated data annotation, yet reliance on expensive commercial models like GPT-4 limits accessibility. This paper rigorously evaluates the potential of open-source smaller LLMs (sLLMs) as a cost-effective alternative. We introduce a new
benchmark dataset, Multidisciplinary Open Research Data (MORD), comprising 12,277 annotated sentence segments from 1,500 scholarly articles across five research domains, to systematically assess sLLM performance. We further
propose to build the Crowd of LLMs, which aggregates annotations from multiple sLLMs using label aggregation algorithms. This approach not only outperforms individual sLLMs but also reveals that combining sLLM annotations with human crowd labels yields superior results compared to either method alone. Our findings highlight the viability of sLLMs for de-ocratizing high-quality data annotation while
underscoring the need for tailored aggregation methods to fully realize their potential.

Aggregating Crowd of LLMs for Cost-Effective Data Annotation

Large Language Models (LLMs) are increasingly used to answer factual, information-seeking questions (ISQs). While prior work often focuses on false, misleading information, little attention has been paid to true but strategically persuasive content that can derail a model’s reasoning. To address this gap, we introduce a new evaluation dataset, TruthTrap, in two languages, i.e., English and Farsi, on Iran-related ISQs, each paired with a correct explanation and a persuasive yet misleading true hint. We then evaluate nine diverse LLMs (spanning proprietary and open-source systems) via factuality classification and multiple-choice QA tasks, finding that accuracy drops by 25%, on average, when models encounter these misleading yet factual hints. Also, the models' predictions match the hint-aligned options up to 77 percent of the time. Notably, models often misjudge such hints in isolation yet still integrate them into final answers. Our results highlight a significant limitation in LLM outputs, underscoring the importance of robust fact-verification and emphasizing real-world risks posed by partial truths in domains like social media, education, and policy-making.

TruthTrap: A Bilingual Benchmark for Evaluating Factually Correct Yet Misleading Information in Question Answering

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. 

MADIAVE: Multi-Agent Debate for Implicit Attribute Value Extraction

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.

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