Morocco

Direct Preference Optimization (DPO) is a powerful approach for aligning large language models (LLMs) with human preferences by formulating preference learning as a supervised classification problem over pairwise human-labeled outputs, thereby enabling stable and efficient training. We show that DPO inherits bias from confounders (e.g., topic, style, user objectives) that shape data generation and carry through to training, hindering recovery of true human preferences. We address this from a causal perspective, proposing Causal Direct Preference Optimization (CDPO), a general framework that incorporates causal inference principles to mitigate the influence of confounders and sharpen the signal of genuine human preferences. Our approach preserves the tractability of direct optimization while enhancing robustness to spurious correlations and annotation biases. Empirical evaluations on benchmark datasets show that CDPO surpasses DPO-based baselines by achieving unbiased fine-tuning through causal reasoning, confirming the effectiveness of confounder-aware preference optimization.

EACL 2026

Causal Direct Preference Optimization for Language Model Alignment

preference learning

large language models

causal inference

## Welcome to 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics!
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The University of Texas at Austin 
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NAACL 2024

2024 Annual Conference of the North American 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. 

Prompts are the interface for eliciting the capabilities of large language models (LLMs). Understanding their structure and components is critical for analyzing LLM behavior and optimizing performance. However, the field lacks a comprehensive framework for systematic prompt analysis and understanding. We introduce PromptPrism, a linguistically-inspired taxonomy that enables prompt analysis across three hierarchical levels: functional structure, semantic component, and syntactic pattern. By applying linguistic concepts to prompt analysis, PromptPrism bridges traditional language understanding and modern LLM research, offering insights that purely empirical approaches might miss. We show the practical utility of PromptPrism by applying it to three applications: (1) a taxonomy-guided prompt refinement approach that automatically improves prompt quality and enhances model performance across a range of tasks; (2) a multi-dimensional dataset profiling method that extracts and aggregates structural, semantic, and syntactic characteristics from prompt datasets, enabling comprehensive analysis of prompt distributions and patterns; (3) a controlled experimental framework for prompt sensitivity analysis by quantifying the impact of semantic reordering and delimiter modifications on LLM performance. Our experimental results validate the effectiveness of our taxonomy across these applications, demonstrating that PromptPrism provides a foundation for refining, profiling, and analyzing prompts.

PromptPrism: A Linguistically-Inspired Taxonomy for Prompts

The strong capabilities of recent Large Language Models (LLMs) have made them highly effective for zero-shot re-ranking task. Attention-based re-ranking methods, which derive relevance scores directly from attention weights, offer an efficient and interpretable alternative to generation-based re-ranking methods. However, they still face two major limitations. First, attention signals are highly concentrated a small subset of tokens within a few documents, making others indistinguishable. Second, attention often overemphasizes phrases lexically similar to the query, yielding biased rankings that irrelevant documents with mere lexical resemblance are regarded as relevant. In this paper, we propose \textbf{ReAttn}, a post-hoc re-weighting strategy for attention-based re-ranking methods. It first compute the cross-document IDF weighting to down-weight attention on query-overlapping tokens that frequently appear across the candidate documents, reducing lexical bias and emphasizing distinctive terms. It then employs entropy-based regularization to mitigate over-concentrated attention, encouraging a more balanced distribution across informative tokens. Both adjustments operate directly on existing attention weights without additional training or supervision. Extensive experiments demonstrate the effectiveness of our method.

ReAttn: Improving Attention-based Re-ranking via Attention Re-weighting

Large Language Models (LLMs) are increasingly deployed in domains such as education, mental health and customer support, where stable and consistent personas are critical for reliability. Yet, existing studies focus on narrative or role-playing tasks and overlook how adversarial conversational history alone can reshape induced personas. Black-box persona manipulation remains unexplored, raising concerns for robustness in realistic interactions. In response, we introduce the task of persona editing, which adversarially steers LLM traits through user-side inputs under a black-box, inference-only setting. To this end, we propose PHISH (Persona Hijacking via Implicit Steering in History), the first framework to expose a new vulnerability in LLM safety that embeds semantically loaded cues into user queries to gradually induce reverse personas. We also define a metric to quantify attack success. Across 3 benchmarks and 8 LLMs, PHISH predictably shifts personas, triggers collateral changes in correlated traits, and exhibits stronger effects in multi-turn settings. In high-risk domains mental health, tutoring, and customer support, PHISH reliably manipulates personas, validated by both human and LLM-as-Judge evaluations. Importantly, PHISH causes only a small reduction in reasoning benchmark performance, leaving overall utility largely intact while still enabling significant persona manipulation. While current guardrails offer partial protection, they remain brittle under sustained attack. Our findings expose new vulnerabilities in personas and highlight the need for context-resilient persona in LLMs. Our codebase and dataset is available at: https://github.com/Jivnesh/PHISH

Persona Jailbreaking in Large Language Models

The paradigm of augmenting Large Language Models (LLMs) with external tools is rapidly converging on the Model-Context Protocol (MCP), a federated architecture of tool "servers'' that mirrors the modern internet. Existing benchmarks, however, remain misaligned with this architecture, typically evaluating agents with APIs in an unstructured, non-hierarchical manner. They often test components like retrieval and reasoning in isolation or sidestep the critical real-world challenge of functional overlap among tools. This leads to an incomplete and overly optimistic assessment of agent capabilities. To address these gaps, we introduceMSC-Bench, a large-scale benchmark for evaluating end-to-end, multi-hop tool use within an explicit, hierarchical MCP ecosystem. A core contribution is our methodology for constructing ground truth by identifying "equal function sets''---groups of functionally equivalent tools. This approach enables the use of objective and reproducible metrics (e.g., F1-score) and mitigates the reliance on fallible LLM-as-a-judge evaluations. MSC-Bench is structured as a five-level curriculum designed to systematically test a full spectrum of agent capabilities, from single-tool execution to complex cross-server orchestration and robustness to out-of-scope requests. Our experiments reveal that rigid hierarchies can hinder performance without co-designed strategies and that even state-of-the-art agents exhibit systemic weaknesses in robustness. MSC-Bench provides a much-needed diagnostic framework to expose these limitations and guide the development of more capable and efficient tool-using systems.

ETOM: A Five-Level Benchmark for Evaluating Tool Orchestration within the MCP Ecosystem

Information Retrieval (IR) methods aim to identify documents relevant to a query, which have been widely applied in various natural language tasks. However, existing approaches typically consider only the textual content within documents, overlooking the fact that documents can contain multiple modalities, including images and tables. Also, they often segment each long document into multiple discrete passages for embedding, which prevents them from capturing the overall document context and interactions between paragraphs. To address these two challenges, we propose a method that holistically embeds documents interleaved with multiple modalities by leveraging the capability of recent vision-language models that enable the processing and integration of text, images, and tables into a unified format and representation. Moreover, to mitigate the information loss from segmenting documents into passages, instead of representing and retrieving passages individually, we further merge the representations of segmented passages into one single document representation, while we additionally introduce a reranking strategy to decouple and identify the relevant passage within the document if necessary. Then, through extensive experiments on diverse IR scenarios considering both the textual and multimodal queries, we show that our approach substantially outperforms relevant baselines, thanks to the consideration of the multimodal information within documents.

Unified Multimodal Interleaved Document Representation for Retrieval

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.

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

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.

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