Austria

Most Large language models (LLMs) adopt an autoregressive architecture, predicting the next word token based on the preceding context. While this approach is robust for language generation tasks such as writing and summarization, it has limitations for high-level reasoning tasks, such as prediction and decision-making. To overcome these limitations, we introduce a new method called Prediction-Augmented Generation (PAG). PAG can improve the generation quality and predictive accuracy of large language models in inference-driven tasks by integrating task-specific predictive models as external tools, enabling more structured and precise reasoning. Moreover, our method does not simply copy the inferences of a predictive model, but improves the inference results with knowledge from the large language model to create better predictions. We comprehensively evaluate our proposed method on diverse datasets for automatic diagnosis tasks requiring extensive domain knowledge and advanced reasoning.

ACL 2025

Prediction-Augmented Generation for Automatic Diagnosis Tasks

nlp for inference

prediction-augmented generation

machine learning for nlp

#### *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)* 

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EACL 2026

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. 

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### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Message from the General Chair: 
*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.* 
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Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Data watermarking in language models injects traceable signals, such as specific token sequences or stylistic patterns, into copyrighted text, allowing copyright holders to track and verify training data ownership. Previous data watermarking techniques primarily focus on effective memorization after pretraining, while overlooking challenges that arise in other stages of the LLM pipeline, such as the risk of watermark filtering during data preprocessing, or potential forgetting through post-training, or verification difficulties due to API-only access. We propose a novel data watermarking approach that injects coherent and plausible yet fictitious knowledge into training data using generated passages describing a fictitious entity and its associated attributes. Our watermarks are designed to be memorized by the LLM through seamlessly integrating in its training data, making them harder to detect lexically during preprocessing. We demonstrate that our watermarks can be effectively memorized by LLMs, and that increasing our watermarks' density, length, and diversity of attributes strengthens their memorization. We further show that our watermarks remain robust throughout LLM development, maintaining their effectiveness after continual pretraining and supervised finetuning. Finally, we show that our data watermarks can be evaluated even under API-only access via question answering.

Robust Data Watermarking in Language Models by Injecting Fictitious Knowledge

Text watermarking, which modify tokens to embed watermark, has proven effective in detecting machine-generated texts. Yet its application to low-entropy texts like code and mathematics presents significant challenges. A fair number of tokens in these texts are hardly modifiable without changing the intended meaning, causing statistical measures to falsely indicate the absence of a watermark. Existing research addresses this issue by rely mainly on a limited number of high-entropy tokens, which are considered flexible for modification, and accurately reflecting watermarks. However, their detection accuracy remains suboptimal, as they neglect strong watermark evidences embedded in low entropy tokens modified through watermarking. To overcome this limitation, we introduce Bayes’ Rule derived Watermark Detector (BRWD), which exploit watermark information from every token, by leveraging the posterior probability of watermark’s presence. We theoretically prove the optimality of our method in terms of detection accuracy, and demonstrate its superiority across various datasets, models, and watermark injection strategies. Notably, our method achieves up to 50% and 70% relative improvements in detection accuracy over the best baselines in code generation and math problem-solving tasks, respectively. Our code is available at https://github.com/cczslp/BRWD.

Low-Entropy Watermark Detection via Bayes’ Rule Derived Detector

Majority voting is considered an effective method to enhance chain-of-thought reasoning, as it selects the answer with the highest ''self-consistency'' among different reasoning paths (Wang et al., 2023). However, previous chain-of-thought reasoning methods typically generate only a single answer in each trial, thereby ignoring the possibility of other potential answers. As a result, these alternative answers are often overlooked in subsequent voting processes. In this work, we propose to generate ranked answers in each reasoning process and conduct ranked voting among multiple ranked answers from different responses, thereby making the overall self-consistency more reliable. Specifically, we use three ranked voting methods: Instant-runoff voting, Borda count voting, and mean reciprocal rank voting. We validate our methods on six datasets, including three multiple-choice and three open-ended question-answering tasks, using both advanced open-source and closed-source large language models. Extensive experimental results indicate that our proposed method outperforms the baselines, showcasing the potential of leveraging the information of ranked answers and using ranked voting to improve reasoning performance. Code and logs will be released.

Ranked Voting based Self-Consistency of Large Language Models

The breakthrough of OpenAI o1 highlights the potential of enhancing reasoning to improve LLM. Yet, most research in reasoning has focused on mathematical tasks, leaving domains like medicine underexplored. The medical domain, though distinct from mathematics, also demands robust reasoning to provide reliable answers, given the high standards of healthcare. However, verifying medical reasoning is challenging, unlike those in mathematics. To address this, we propose **Medical Verifiable Problems** with a medical verifier to check the correctness of model outputs. This verifiable nature enables advancements in medical reasoning through **a two-stage approach**: (1) using the verifier to guide the search for a complex reasoning trajectory for fine-tuning LLMs, (2) applying reinforcement learning (RL) with verifier-based rewards to enhance complex reasoning further. Finally, we introduce HuatuoGPT-o1, a medical LLM capable of complex reasoning, which outperforms general and medical-specific baselines using only 40K verifiable problems. Experiments show complex reasoning improves medical problem-solving and benefits more from RL. We hope our approach inspires advancements in reasoning across medical and other specialized domains. Code, datasets, and models are publicly available at https://github.com/FreedomIntelligence/HuatuoGPT-o1.

Towards Medical Complex Reasoning with LLMs through Medical Verifiable Problems

In recent progress, mathematical verifiers have achieved success in mathematical reasoning tasks by validating the correctness of solutions generated by policy models. However, existing verifiers are trained with binary classification labels, which are not informative enough for the model to accurately assess the solutions. To mitigate the aforementioned insufficiency of binary labels, we introduce step-wise natural language feedback as rationale labels, that is, the correctness of each step and the detailed explanations. In this paper, we propose Math-Minos, a natural language feedback-enhanced verifier by constructing automatically generated training data and a two-stage training paradigm for effective training and efficient inference. Our experiments reveal that a small set of natural language feedback can significantly boost the performance of the verifier in both verification and reinforcement learning and also significantly alleviates the data-demanding problems of the reward model with an over 700% data efficiency improvement.

LLM Critics Help Catch Bugs in Mathematics: Towards a Better Mathematical Verifier with Natural Language Feedback

Question Answering (QA) with large language models has shown impressive performance, yet hallucinations still persist, particularly when user queries carry incorrect premises, insufficient context, or linguistic ambiguity. To address this issue, we propose Lightweight Query Checkpoint (LQC), a small classification model that detects verification-required queries before the LLM generates a potentially faulty answer. LQC leverages hidden states extracted from intermediate layers of a smaller-scale, non-instruct-tuned LLM to effectively distinguish queries requiring verification from clear queries. We first systematically define categories of queries that need verification, construct a dataset comprising both defective and clear queries, and train a binary contrastive learning model. Through extensive experiments on various QA datasets, we demonstrate that incorporating LQC into QA pipelines reduces hallucinations while preserving strong answer quality.

Lightweight Query Checkpoint: Classifying Faulty User Queries to Mitigate Hallucinations in Large Language Model Question Answering

In-hospital text data contains valuable clinical information, yet deploying fine-tuned small language models (SLMs) for information extraction remains challenging due to differences in formatting and vocabulary across institutions. Since access to the original in-hospital data (source domain) is often restricted, annotated data from the target hospital (target domain) is crucial for domain adaptation. However, clinical annotation is notoriously expensive and time-consuming, as it demands clinical and linguistic expertise. To address this issue, we leverage large language models (LLMs) to annotate the target domain data for the adaptation. We conduct experiments on four clinical information extraction tasks, including eight target domain data. Experimental results show that LLM-annotated data consistently enhances SLM performance and, with a larger number of annotated data, outperforms manual annotation in three out of four tasks.

Exploring LLM Annotation for Adaptation of Clinical Information Extraction Models under Data-sharing Restrictions

The scarcity of publicly available clinical corpora hinders developing and applying NLP tools in clinical research. While existing work tackles this issue by utilizing generative models to create high-quality synthetic corpora, their methods require learning from the original in-hospital clinical documents, turning them unfeasible in practice. To address this problem, we introduce RecordTwin, a novel synthetic corpus creation method designed to generate synthetic documents from anonymized clinical entities. In this method, we first extract and anonymize entities from in-hospital documents to ensure the information contained in the synthetic corpus is restricted. Then, we use a large language model to fill the context between anonymized entities. To do so, we use a small, privacy-preserving subset of the original documents to mimic their formatting and writing style. This approach only requires anonymized entities and a small subset of original documents in the generation process, making it more feasible in practice. To evaluate the synthetic corpus created with our method, we conduct a proof-of-concept study using a publicly available clinical database. Our results demonstrate that the synthetic corpus has a utility comparable to the original data and a safety advantage over baselines, highlighting the potential of RecordTwin for privacy-preserving synthetic corpus creation.

RecordTwin: Towards Creating Safe Synthetic Clinical Corpora

Language models (LMs) risk inadvertently memorizing and divulging sensitive or personally identifiable information (PII) seen in training data, causing privacy concerns. Current approaches to address this issue involve costly dataset scrubbing, or model filtering through unlearning and model editing, which can be bypassed through extraction attacks. We propose REVS, a novel non-gradient-based method for unlearning sensitive information from LMs. REVS identifies and modifies a small subset of neurons relevant for constituent tokens which form sensitive information. To adequately evaluate our method on truly sensitive information, we curate three datasets: an email and URL datasets naturally memorized by the models, and a synthetic social security number dataset that we tune the models to memorize. Compared to other methods, REVS demonstrates superior performance in unlearning sensitive information and robustness to extraction attacks, while retaining underlying model integrity.

REVS: Unlearning Sensitive Information in Language Models via Rank Editing in the Vocabulary Space

Large Language Model (LLM) agents have demonstrated impressive capabilities in handling complex interactive problems. Existing LLM agents mainly generate natural language plans to guide reasoning, which is verbose and inefficient. NL plans are also tailored to specific tasks and restrict agents’ ability to generalize across similar tasks. To this end, we explore pseudocode-style plans (P-code Plan) to capture the structural logic of reasoning. We find that P-code Plan empowers LLM agents with stronger generalization ability and more efficiency. Inspired by this finding, we propose a pseudocode-style ̲Planning ̲Guided ̲Preference ̲Optimization method called PGPO for effective agent learning. With two planning-oriented rewards, PGPO further enhances LLM agents’ ability to generate high-quality P-code Plans and subsequent reasoning. Experiments show that PGPO achieves superior performance on representative agent benchmarks and outperforms the current leading baselines. Analyses reveal the advantage of PGPO in reducing action errors and omissions during reasoning.

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