China

Understanding temporal relations between events in a text is essential for determining its temporal structure. Recent advancements in large language models (LLMs) have spurred research on temporal relation extraction. However, LLMs perform poorly in zero-shot and few-shot settings, often underperforming smaller fine-tuned models. Despite these limitations, little attention has been given to improving LLMs in temporal structure extraction tasks. This study systematically examines LLMs’ ability to extract and infer discourse-level temporal relations, identifying factors influencing their reasoning and extraction capabilities, including input context, reasoning process and ensuring consistency. We propose a three-step framework to improve LLMs&#39; temporal relation extraction capabilities: context selection, prompts inspired by Allen&#39;s interval algebra (Allen, 1983), and reflection-based consistency learning (Shinn et al., 2024). Our results show the effectiveness of our method in guiding LLMs towards structured processing of temporal structure in discourse.

EMNLP 2025

Consistent Discourse-level Temporal Relation Extraction Using Large Language Models

temporal relation extraction

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The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. Our Virtual Poster Sessions will take place online Thursday, August 22, 2024.

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ACL 2024

The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

Understanding temporal relations between events in a text is essential for determining its temporal structure. Recent advancements in large language models (LLMs) have spurred research on temporal relation extraction. However, LLMs perform poorly in zero-shot and few-shot settings, often underperforming smaller fine-tuned models. Despite these limitations, little attention has been given to improving LLMs in temporal structure extraction tasks. This study systematically examines LLMs’ ability to extract and infer discourse-level temporal relations, identifying factors influencing their reasoning and extraction capabilities, including input context, reasoning process and ensuring consistency. We propose a three-step framework to improve LLMs' temporal relation extraction capabilities: context selection, prompts inspired by Allen's interval algebra (Allen, 1983), and reflection-based consistency learning (Shinn et al., 2024). Our results show the effectiveness of our method in guiding LLMs towards structured processing of temporal structure in discourse.

poster

## Welcome!
"I am excited to welcome you to this year’s edition of the Conference on Empirical Methods in Natural Language Processing! Importantly, it marks the 30th edition of EMNLP. With over 8,000 submissions, more than 3,000 accepted papers, and thousands of attendees, we have come a long way from that first
workshop, which had 14 accepted papers. As the field looks ahead, Suzhou is the fitting location for celebrating this milestone: rooted in a long literary tradition, yet modern and forward-looking, and home to a large share of the NLP community." 

*Message from the General Chair, Dirk Hovy*

Celebrate 30 Years of EMNLP! 
EMNLP 2025 will be held in Suzhou, China from November 5th to November 9th, 2025.

Large vision-language models (LVLMs) have shown remarkable performance in visual-language understanding for downstream multimodal tasks. While their capabilities are improving, problems emerge simultaneously. Among those problems, the hallucinations have attracted much attention, which stands for the phenomenon where LVLMs generate contradictory content to their input visual and text contents. Many approaches have been proposed to deal with this issue, such as contrastive decoding and attention manipulation. However, contrastive decoding methods struggle in constructing appropriate contrastive samples, and attention manipulation methods are highly sensitive, lacking stability. In this work, we propose image head Masked Contrastive Decoding (MaskCD). Our approach utilizes the "image heads" in LVLMs, masking them to construct contrastive samples for contrastive decoding. We evaluated MaskCD on LLaVA-1.5-7b and Qwen-VL-7b, using various benchmarks such as CHAIR, POPE, and MME. The results demonstrate that MaskCD effectively alleviates the phenomenon of hallucinations and retains the general capabilities of LVLMs.

MaskCD: Mitigating LVLM Hallucinations by Image Head Masked Contrastive Decoding

Gradient-based data influence approximation has been leveraged to select useful data samples in the supervised fine-tuning of large language models. However, the computation of gradients throughout the fine-tuning process requires too many resources to be feasible in practice. In this paper, we propose an efficient gradient-based data selection framework with clustering and a modified Upper Confidence Bound (UCB) algorithm. Based on the intuition that data samples with similar gradient features will have similar influences, we first perform clustering on the training data pool. Then, we frame the inter-cluster data selection as a constrained computing budget allocation problem and consider it a multi-armed bandit problem. A modified UCB algorithm is leveraged to solve this problem. Specifically, during the iterative sampling process, historical data influence information is recorded to directly estimate the distributions of each cluster, and a cold start is adopted to balance exploration and exploitation. Experimental results on various benchmarks show that our proposed framework, ClusterUCB, can achieve comparable results to the original gradient-based data selection methods while greatly reducing computing consumption.

ClusterUCB: Efficient Gradient-Based Data Selection for Targeted Fine-Tuning of LLMs

The development of Large Language Models (LLMs) requires robust benchmarks that encompass not only academic domains but also industrial fields to effectively evaluate their applicability in real-world scenarios. In this paper, we introduce two Korean expert-level benchmarks. KMMLU-Redux, reconstructed from the existing KMMLU consists of questions from the Korean National Technical Qualification exams, with critical errors removed to enhance reliability. KMMLU-Pro is based on Korean National Professional Licensure exams to reflect professional knowledge in Korea. Our experiments demonstrate that these benchmarks comprehensively represent industrial knowledge in Korea.

From KMMLU-Redux to Pro: A Professional Korean Benchmark Suite for LLM Evaluation

The Impression section of a radiology report summarizes critical findings of a radiology report and thus plays a crucial role in communication between radiologists and physicians. Research on radiology report summarization mostly focuses on generating the Impression section by summarizing information from the Findings section, which typically details the radiologist's observations in the radiology images. Recent work start to explore how to incorporate radiology images as input to multimodal summarization models, with the assumption that it can improve generated summary quality, as it contains richer information. However, the real effectiveness of radiology images remains unclear. To answer this, we conduct a thorough analysis to understand whether current multimodal models can utilize radiology images in summarizing Findings section. Our analysis reveals that current multimodal models often fail to effectively utilize radiology images. For example, masking the image input leads to minimal or no performance drop. Expert annotation study shows that radiology images are unnecessary when they write the Impression section.

The More, The Better? A Critical Study of Multimodal Context in Radiology Report Summarization

Large Language Models (LLMs) excel at reasoning but often struggle with multi-step tasks due to limited contextual understanding. While Retrieval-Augmented Generation (RAG) provides external context, it relies on static datasets and struggles to generalize across diverse queries. In this work, we propose a Generative Retrieval-Aligned Demonstrator (GRAD), a dynamic demonstration-based approach where an LLM model is trained to generate input-specific concise demonstrations. By tailoring demonstrations to each input, our method offers better contextual support than traditional RAG approaches. GRAD is grounded in a generative strategy allowing adaptive prompting without needing external retrieval. We demonstrate the superiority of GRAD under the budget constraints, where we limit both the number of tokens per demonstration and the output response budget. Trained solely on math dataset, GRAD consistently outperforms strong baselines ranging from math reasoning to advanced STEM questions on Qwen2.5-14B. This showcases the robust generalization of GRAD to out-of-distribution (OOD) STEM tasks, such as physics, chemistry and computer science. Since demonstrations can be generated by a different, smaller model, GRAD might reduce training cost while still maintaining similar accuracy. This work introduces a scalable demonstration generator model that is the first step towards a new few-shot learning paradigm in constrained, limited-resource settings.

GRAD: Generative Retrieval-Aligned Demonstration Sampler for Efficient Few-Shot Reasoning

In scientific research, “limitations” refer to the shortcomings, constraints, or weaknesses within a study. A transparent reporting of such limitations can enhance the quality and reproducibility of research and improve public trust in science. However, authors often a) underreport them in the papers’ text and b) use hedging strategies to satisfy editorial requirements at the cost of readers’ clarity and confidence. This underreporting behavior, along with an explosion in the number of publications, has created a pressing need to automatically extract/generate such limitations from scholarly papers. In that direction, we report a complete architecture for computational analysis of research limitations. Specifically, we a) create a dataset of limitations in ACL, NeurIPS, and PeerJ papers by extracting them from papers’ text and integrating them with external reviews; b) propose methods to automatically generate them using a novel Retrieval Augmented Generation (RAG) technique; c) create a fine-grained evaluation framework for generated limitations and provide a meta-evaluation for the proposed evaluation techniques.

BAGELS: Benchmarking the Automated Generation and Extraction of Limitations from Scholarly Text

Procedural text adaptation—such as modifying recipes or revising instructional guides—has traditionally relied on specialized models extensively fine‑tuned for specific domains. To address the scalability limitations of such approaches, recent research has increasingly turned to general‑purpose large language models (LLMs). However, existing prompting strategies for LLMs often yield superficial or erroneous adaptations due to alignment‑induced biases and the inherent complexity of procedural editing. To overcome these challenges, we propose the Over‑generation‑and‑Compaction (OC) prompting strategy, which first elicits an exhaustive set of procedural details to leverage the model’s latent knowledge, and subsequently compacts them into concise, coherent adaptations. We further introduce Recipe Consistency & Feasibility (RCF), a novel metric for systematically assessing procedural validity and practicality in cooking recipe adaptations. Experiments on public datasets demonstrate that OC significantly improves adaptation consistency and feasibility compared to baseline prompting methods, without the need for additional fine-tuning or curated training resources.

Over-Generation and Compaction: A Prompting Strategy for Procedural Text Adaptation with Large Language Models

The scarcity of non-English language data in specialized domains significantly limits the development of effective Natural Language Processing (NLP) tools. We present TransBERT, a novel framework for pre-training language models using exclusively synthetically translated text, and introduce TransCorpus, a scalable translation toolkit. Focusing on the life sciences domain in French, our approach demonstrates that state-of-the-art performance on various downstream tasks can be achieved solely by leveraging synthetically translated data. We release the TransCorpus toolkit, the TransCorpus-bio-fr corpus (36.4GB of French life sciences text), TransBERT-bio-fr, its associated pre-trained language model and reproducible code for both pre-training and fine-tuning. Our results highlight the viability of synthetic translation for building high-quality NLP resources in low-resource language/domain pairs.

TransBERT: A Framework for Synthetic Translation in Domain-Specific Language Modeling

Large Language Models (LLMs) excel at generating synthetic data, but ensuring its quality and diversity remains challenging. We propose Genetic Prompt, a novel framework that innovatively combines genetic algorithms (GAs) with LLMs for synthetic data generation. Unlike previous GA approaches, Genetic Prompt implicitly represents semantic text attributes as gene sequences. Additionally, We implement an active learning scheme to optimize the GA process, eliminating traditional fitness evaluation and expanding offspring search space. Experiments on four relation extraction datasets show that Genetic Prompt significantly outperforms State-of-the-art baselines. Further more, analysis reveals that Genetic Prompt generates more diverse data with distributions closer to the gold dataset.

Attributes as Textual Genes: Leveraging LLMs as Genetic Algorithm Simulators for Conditional Synthetic Data Generation

Evaluating text generation capabilities of large language models (LLMs) is challenging, particularly for low-resource languages where methods for direct assessment are scarce. We propose MUG-Eval, a novel framework that evaluates LLMs' multilingual generation capabilities by transforming existing benchmarks into conversational tasks and measuring the LLMs' accuracies on those tasks. We specifically designed these conversational tasks to require effective communication in the target language. Then, we simply use task success rate as a proxy of successful conversation generation. Our approach offers two key advantages: it is independent of language-specific NLP tools or annotated datasets, which are limited for most languages, and it does not rely on LLMs-as-judges, whose evaluation quality degrades outside a few high-resource languages. We evaluate 8 LLMs across 30 languages spanning high, mid, and low-resource categories, and we find that MUG-Eval correlates strongly with established benchmarks (r > 0.75) while enabling standardized comparisons across languages and models. Our framework provides a robust and resource-efficient solution for evaluating multilingual generation that can be extended to thousands of languages.

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MaskCD: Mitigating LVLM Hallucinations by Image Head Masked Contrastive Decoding

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