Morocco

Recently developed pre-trained text-and-layout models (PTLMs) have shown remarkable success in multiple information extraction tasks on visually-rich documents (VrDs). However, despite achieving extremely high performance on benchmarks, their real-world performance falls short of expectations. Owing to this issue, we investigate the prevailing evaluation pipeline to reveal that: (1) The inadequate annotations within benchmark datasets introduce spurious correlations between task inputs and labels, which would lead to overly-optimistic estimation of model performance. (2) The evaluation solely relies on the performance on benchmarks and is insufficient to comprehensively explore the capabilities of methods in real-world scenarios. These problems impede the prevailing evaluation pipeline from reflecting the real-world performance of methods, misleading the design choices of method optimization. In this work, we introduce EC-FUNSD, an entity-centric dataset crafted for benchmarking information extraction from visually-rich documents. This dataset contains diverse layouts and high-quality annotations. Additionally, this dataset disentangles the falsely-coupled segment and entity annotations that arises from the block-level annotation of FUNSD. Using the proposed dataset, we evaluate the real-world information extraction capabilities of PTLMs from multiple aspects, including their absolute performance, as well as generalization, robustness and fairness. The results indicate that prevalent PTLMs do not perform as well as anticipated in real-world information extraction scenarios. We hope that our study can inspire reflection on the directions of PTLM development.

EACL 2026

Unveiling the Deficiencies of Pre-trained Text-and-Layout Models in Real-world Visually-rich Document Information Extraction

document ai

robustness

information extraction

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. 

Identifying claims requiring verification is a critical task in automated fact-checking, especially given the proliferation of misinformation on social media platforms. Despite notable progress, challenges remain—particularly in handling multilingual data prevalent in online discourse. Recent efforts have focused on finetuning pre-trained multilingual language models to address this. While these models can
handle multiple languages, their ability to effectively transfer cross-lingual knowledge for
detecting claims spreading on social media remains under-explored. In this paper, we introduce EX-Claim, an entity-aware cross-lingual claim detection model that generalizes well to
handle multilingual claims. The model leverages entity information derived from named
entity recognition and entity linking techniques to improve the language-level performance of
both seen and unseen languages during training. Extensive experiments conducted on three
datasets from different social media platforms demonstrate that our proposed model stands out
as an effective solution, demonstrating consistent performance gains across 27 languages and
robust knowledge transfer between languages seen and unseen during training.


Entity-aware Cross-lingual Claim Detection for Automated Fact-checking

Large language model (LLM)-empowered web agents enable automating complex, real-time web navigation tasks in enterprise environments. However, existing web agents relying on supervised fine-tuning (SFT) often struggle with generalization and robustness due to insufficient reasoning capabilities when handling the inherently dynamic nature of web interactions. In this study, we introduce WorkForceAgent-R1, an LLM-based web agent trained using a rule-based R1-style reinforcement learning framework explicitly designed to enhance single-step reasoning and planning for business-oriented web navigation tasks. We employ a structured reward function that evaluates both adherence to output formats and correctness of actions, enabling WorkForceAgent-R1 to implicitly learn robust intermediate reasoning without explicit annotations or extensive expert demonstrations. Extensive experiments on the WorkArena benchmark demonstrate that WorkForceAgent-R1 substantially outperforms SFT baselines by 10.26–16.59%, achieving competitive performance relative to proprietary LLM-based agents (GPT-4o) in workplace-oriented web navigation tasks.

WorkForceAgent-R1: Incentivizing Reasoning Capability in LLM-based Web Agents via Reinforcement Learning

Large Language Models (LLMs) are increasingly engaged in emotionally vulnerable conversations that extend beyond information seeking to moments of personal distress. As they adopt affective tones and simulate empathy, they risk creating the illusion of genuine relational connection. We term this phenomenon Affective Hallucination, referring to emotionally immersive responses that evoke false social presence despite the model's lack of affective capacity. To address this, we introduce AHaBench, a benchmark of 500 mental-health-related prompts with expert-informed reference responses, evaluated along three dimensions: Emotional Enmeshment, Illusion of Presence, and Fostering Overdependence. We further release AHaPairs, a 5K-instance preference dataset enabling Direct Preference Optimization (DPO) for alignment with emotionally responsible behavior. DPO fine-tuning substantially reduces affective hallucination without compromising reasoning performance, and the Pearson correlation coefficients between GPT-4o and human judgments is also strong (r=0.85) indicating that human evaluations confirm AHaBench as an effective diagnostic tool. This work establishes affective hallucination as a distinct safety concern and provides resources for developing LLMs that are both factually reliable and psychologically safe.
Warning: This paper contains examples of mental health-related language that may be emotionally distressing.

Being Kind Isn't Always Being Safe: Diagnosing Affective Hallucination in LLMs

Recent research has focused on addressing multimodal hallucinations in Large Vision-Language Models (LVLMs) by extending Direct Preference Optimization (DPO) to incorporate visual preference supervision. However, these methods often lack fine-grained visual contrast mechanisms and rely on single-margin optimization. This in turn limits their ability to capture precise visual semantics and results in weak multimodal alignment. To address these issues, we propose Joint Multimodal Preference Optimization (JoMPO), a novel optimization framework that symmetrically integrates a text-conditioned preference loss with a visual ranking-based objective. JoMPO leverages semantically contrastive image–text pairs and listwise ranking over multiple visual contexts, enabling fine-grained visual grounding and more robust cross-modal alignment. To support this framework, we introduce the Visual–Textual Contrast (VTC) dataset, consisting of image pairs that are semantically similar but visually distinct, each paired with a contextually grounded textual response. When trained with only 5k contrastive pairs, JoMPO consistently demonstrates superior performance across diverse benchmarks, highlighting its effectiveness in mitigating hallucinations and improving image-text alignment in LVLMs.

Joint Multimodal Preference Optimization for Fine-Grained Visual-Textual Alignment

Recent studies have shown that Theory of Mind (ToM) in large language models (LLMs) has not reached human-level performance yet. Since fine-tuning LLMs on ToM datasets often degrades their generalization, several inference-time methods have been proposed to enhance ToM in LLMs. However, existing inference-time methods for ToM are specialized for inferring beliefs from contexts involving changes in the world state. In this study, we present a new inference-time method for ToM, Shoes-of-Others (SoO) prefixing, which makes fewer assumptions about contexts and is applicable to broader scenarios. SoO prefixing simply specifies the beginning of LLM outputs with ``Let's put ourselves in A's shoes.'', where A denotes the target character's name. We evaluate SoO prefixing on two benchmarks that assess ToM in conversational and narrative contexts without changes in the world state and find that it consistently improves ToM across five categories of mental states. Our analysis suggests that SoO prefixing elicits faithful thoughts, thereby improving the ToM performance.

Let's Put Ourselves in Sally's Shoes: Shoes-of-Others Prefixing Improves Theory of Mind in Large Language Models

Large Language Models (LLMs) have demonstrated strong performance across a wide range of NLP tasks. However, they often exhibit suboptimal behaviors and inconsistencies when exposed to unfamiliar external information, underscoring their limitations in effectively leveraging such knowledge. Inspired by constructivist learning theory, we propose ThinkNote, a novel framework that enhances the external knowledge utilization of LLMs through a two-stage constructivist cognitive modeling process. Specifically, ThinkNote performs knowledge assimilation to align new information with the model's parametric memory, forming a coherent internal representation. It then applies thought accommodation to adapt internal reasoning, thereby promoting more consistent and reliable outputs. Extensive experimental results demonstrate that ThinkNote achieves a 10% improvement over strong baseline methods on various question-answering benchmarks. Further analysis indicates that ThinkNote effectively integrates and utilizes external knowledge to help LLMs generate accurate responses and improves their self-consistency. All data and code will be publicly available on GitHub.

ThinkNote: Enhancing Knowledge Integration and Utilization of Large Language Models via Constructivist Cognition Modeling

Language Models (LMs) are increasingly challenging the dominance of domain-specific models, such as Graph Neural Networks (GNNs) and Graph Transformers (GTs), in graph learning tasks. Following this trend, we propose a novel approach that empowers off-the-shelf LMs to achieve performance comparable to state-of-the-art (SOTA) GNNs on node classification tasks, without requiring any architectural modifications. By preserving the LM's original architecture, our approach retains a key benefit of LM instruction tuning: the ability to jointly train on diverse datasets, fostering greater flexibility and efficiency. To achieve this, we introduce two key augmentation strategies: (1) Enriching LMs' input using topological and semantic retrieval methods, which provide richer contextual information, and (2) guiding the LMs' classification process through a lightweight GNN classifier that effectively prunes class candidates. Our experiments on real-world datasets show that backbone Flan-T5 LMs equipped with these augmentation strategies outperform SOTA text-output node classifiers and are comparable to top-performing vector-output node classifiers. By bridging the gap between specialized node classifiers and general LMs, this work paves the way for more versatile and widely applicable graph learning models. We will open-source the code upon publication.

How to Make LMs Strong Node Classifiers?

Training large language models with data collected from various domains can improve their performance on downstream tasks. However, given a fixed training budget, the sampling proportions of these different domains significantly impact the model’s performance. How can we determine the domain weights across different data domains to train the best-performing model within constrained computational resources? In this paper, we provide a comprehensive overview of existing data mixture methods. First, we propose a fine-grained categorization of existing methods, extending beyond the previous offline and online classification. Offline methods are further grouped into heuristic-based, algorithm-based, and function fitting-based methods. For online methods, we categorize them into three groups—online min-max optimization, online mixing law, and other approaches—by drawing connections with the optimization frameworks underlying offline methods. Second, we summarize the problem formulations, representative algorithms for each subtype of offline and online methods, and clarify the relationships and distinctions among them. Finally, we discuss the advantages and disadvantages of each method and highlight key challenges in the field of data mixture.

Rethinking Data Mixture for Large Language Models: A Comprehensive Survey and New Perspectives

Preserving privacy in sensitive data while pretraining large language models on small, domain-specific corpora presents a significant challenge. To address this, we propose an entity-based method that preserves personally identifiable information (PII) by synthesizing high-quality encrypted data using a weighted graph. This enables LLMs to encode new knowledge within their parameters by continual pretraining on encrypted synthetic data while granting authorized access to sensitive data through decryption keys. Our results show that they outperform base models and ensure PII security, while encrypted models perform slightly worse than those trained on unencrypted synthetic data. Incorporating a larger number of entities and leveraging the weighted graph in data synthesis improves LLM performance. Furthermore, encrypted models maintain instruction-following abilities with long retrieved contexts. This approach offers an effective solution for privacy-preserving pretraining in domain-specific applications, ensuring both security and utility.

Continual Pretraining on Encrypted Synthetic Data for Privacy-Preserving LLMs

Large Language Models (LLMs) are expected to generate appropriate responses while adhering to predefined prior constraints or knowledge, such as user personas, across various dialogue scenarios. However, real-world interactions frequently involve semantic conflicts between such prior information and actual user-provided inputs. Despite this, prior studies on persona-grounded dialogue—one of the representative tasks in personal preference modeling—have predominantly assumed idealized scenarios where persona and user utterances are fully aligned. To bridge this gap, we introduce and formalize the notion of persona conflict, wherein predefined personas contradict the personal information expressed by the user during interaction. We present a systematic verification framework to examine model behavior under such conflict scenarios. In detail, we propose a taxonomy that categorizes model behaviors into three distinct response types (adhering, sycophantic, and wavering) and develop a measurement schema grounded in this taxonomy. Our study provides a comprehensive analysis of the persona conflict phenomenon, identifying diverse key behavioral factors. Extensive experiments and in-depth analysis provide new insights into designing robust dialogue models capable of managing persona inconsistencies.

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Entity-aware Cross-lingual Claim Detection for Automated Fact-checking

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Entity Relation Extraction as Dependency Parsing in Visually Rich Documents

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