China

Large language models (LLMs) achieve remarkable advancements by leveraging tools to interact with environments, a critical step toward generalized AI. However, the standard supervised fine-tuning (SFT) approach, which relies on large-scale datasets, often overlooks task-specific characteristics in tool use, leading to performance bottlenecks. To address this issue, we analyze three existing LLMs and uncover key insights: training data can inadvertently impede tool-use behavior, token importance is distributed unevenly, and errors in tool calls fall into a small set of categories. Building on these findings, we propose \textit{TL-Training}, a task-feature-based framework that mitigates the effects of suboptimal training data, dynamically adjusts token weights to prioritize key tokens during SFT, and incorporates a robust reward mechanism tailored to error categories, optimized through proximal policy optimization. We validate TL-Training by training CodeLLaMA-2-7B and evaluating it on four open-source test sets. Our results demonstrate that the LLM trained by our method matches or surpasses both open- and closed-source LLMs in tool-use performance using only 1,217 training data points. Additionally, our method enhances robustness in noisy environments and improves general task performance, offering a scalable and efficient paradigm for tool-use training in LLMs.

EMNLP 2025

TL-Training: A Task-Feature-Based Framework for Training Large Language Models in Tool Use

task-feature-based framework

tool use

large language models

## Welcome to EMNLP 2024! 
We are excited to welcome you to one of the most prominent conferences in the field of Natural Language Processing. This year, EMNLP 2024 is being held in a hybrid format,
offering both virtual and in-person participation in beautiful Miami. Due to a record-breaking number of submissions, we've expanded the total number of accepted papers to accommodate more cutting-edge research from around the globe.
### [Conference Handbook](https://drive.google.com/file/d/1WPROgxjLAC96AJL7Ugy0tEnYm7dkrbHt/view?usp=sharing)

You are required to register for this event. **Please register [here](https://2024.emnlp.org/registration/).** The EMNLP 2024 event page on Underline will be open to public one week prior to the event.

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EMNLP 2024

EMNLP 2024 will take place in Miami, Florida from Nov 12th to Nov 16th, 2024, at the Hyatt Regency Miami Hote and on Underline for remote participants.

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.

Intelligent cockpit systems represent a unique challenge for API Agents due to their interconnected, state-dependent subsystems that exceed the complexity of typical task environments. Traditional function calling approaches operate statelessly, requiring multiple exploratory calls to build environmental awareness before execution, leading to inefficiency and limited error recovery. We introduce VehicleWorld, the first comprehensive environment for the automotive domain that provides real-time state information during agent execution and enables precise evaluation of vehicle agent behaviors. Based on VehicleWorld, we construct a series of challenging scenarios and problems to benchmark agent performance. Through our analysis, we observe that directly predicting environment states is more effective than predicting function calls to change the environment. Building on this insight, we propose State-based Function Calling (SFC), which explicitly maintains system state awareness and implements direct state transitions to achieve target conditions. Our experimental results demonstrate that SFC serves as a strong baseline that significantly outperforms traditional function calling approaches across multiple metrics, including execution accuracy and latency reduction. By providing comprehensive state information, SFC enables models to better interpret user intentions while substantially reducing erroneous and redundant calls, advancing more reliable agent-vehicle interactions in intelligent cockpit environment.

VehicleWorld: A Highly Integrated Multi-Device Environment for Intelligent Vehicle Interaction

The rapid advancement of perovskite solar cells (PSCs) has led to an exponential growth in research publications, creating an urgent need for efficient knowledge management and reasoning systems in this domain. We present a comprehensive knowledge-enhanced system for PSCs that integrates three key components. First, we develop Perovskite-KG, a domain-specific knowledge graph constructed from 1,517 research papers, containing 23,789 entities and 22,272 relationships. Second, we create two complementary datasets: Perovskite-Chat, comprising 55,101 high-quality question-answer pairs generated through a novel multi-agent framework, and Perovskite-Reasoning, containing 2,217 carefully curated materials science problems. Third, we introduce two specialized large language models: Perovskite-Chat-LLM for domain-specific knowledge assistance and Perovskite-Reasoning-LLM for scientific reasoning tasks. Experimental results demonstrate that our system significantly outperforms existing models in both domain-specific knowledge retrieval and scientific reasoning tasks, providing researchers with effective tools for literature review, experimental design, and complex problem-solving in PSC research.

Perovskite-LLM: Knowledge-Enhanced Large Language Models for Perovskite Solar Cell Research

Question answering on the hybrid context of tables and text (TATQA) is a critical task, with broad applications in data-intensive domains. However, existing TATQA datasets are limited to English, leading to several drawbacks: (i) They overlook the challenges of multilingual TAT-QA and cannot assess model performance in the multilingual setting. (ii) They do not reflect real-world multilingual scenarios where tables and texts frequently appear in non-English languages. To address the limitations, we propose the first multilingual TATQA dataset (MULTITAT). Specifically, we sample data from 3 mainstream TATQA datasets and translate it into 10 diverse languages. To align the model TATQA capabilities in English with other languages, we develop a baseline, Ours. Experimental results reveal that the performance on non-English data in MULTITAT drops by an average of 19.4% compared to English, proving the necessity of MULTITAT. We further analyze the reasons for this performance gap. Furthermore, Ours outperforms other baselines by an average of 3.3, demonstrating its effectiveness.

MULTITAT: Benchmarking Multilingual Table-and-Text Question Answering

Text-guided image editing has seen rapid progress in natural image domains, but its adaptation to medical imaging remains limited and lacks standardized evaluation. Clinically, such editing holds promise for simulating surgical outcomes, creating personalized teaching materials, and enhancing patient communication. To bridge this gap, we introduce MedEBench, a comprehensive benchmark for evaluating text-guided medical image editing. It consists of 1,182 clinically sourced image-prompt triplets spanning 70 tasks across 13 anatomical regions. MedEBench offers three key contributions: (1) a clinically relevant evaluation framework covering Editing Accuracy, Contextual Preservation, and Visual Quality, supported by detailed descriptions of expected change and ROI (Region of Interest) masks; (2) a systematic comparison of seven state-of-the-art models, revealing common failure patterns; and (3) a failure analysis protocol based on attention grounding, using IoU (Intersection over Union Ratio) between attention maps and ROIs to identify mislocalization. MedEBench provides a solid foundation for developing and evaluating reliable, clinically meaningful medical image editing systems.

MedEBench: Diagnosing Reliability in Text-Guided Medical Image Editing

Mixture-of-Experts (MoE) has emerged as a promising approach for scaling large language models efficiently. However, how to design a desired MoE architecture given performance, efficiency, or safety goals remains absent. Existing benchmarks often focus on isolated aspects (e.g., reasoning, efficiency, safety), and there is a lack of consensus on optimal design choices, such as the number and size of experts, the type of routers, and the regularization during pre-training, or strategies like freezing, learning rate adjustments, and limiting expert collaboration during fine-tuning, with prior works often yielding conflicting conclusions. Motivated by this research gap, we introduce MoEBench, the first comprehensive assessment of MoE designs across the three dimensions of reasoning ability, efficiency, and safety. Our benchmark systematically evaluates optimal architectural choices during both pre-training and fine-tuning phases. We evaluate two popular MoE backbones across four dimensions of design choices on over eight metrics. Our empirical findings uncover hidden underlying correlations among MoE design choices. Specifically, we observe that (1) token-level routing and z-loss regularization improve reasoning performance; (2) shared experts enhance training stability but reduce specialization; and (3) collaboration-constrained routing and freezing strategies significantly influence load balance, specialization, and safety alignment. Furthermore, we propose three "sweet point" combinations of optimal strategies tailored to different scenarios. We hope this study provides actionable insights for building more robust, efficient, and secure MoE models. Code, checkpoints, and raw data will be released upon acceptance of the paper.

Bag of Tricks for Sparse Mixture-of-Experts: A Benchmark Across Reasoning, Efficiency, and Safety

Large language models (LLMs) have witnessed rapid advancements, demonstrating remarkable capabilities. However, a notable vulnerability persists: LLMs often uncritically accept flawed or contradictory premises, leading to inefficient reasoning and unreliable outputs. This emphasizes the significance of possessing the **Premise Critique Ability** for LLMs, defined as the capacity to proactively identify and articulate errors in input premises. Most existing studies assess LLMs' reasoning ability in ideal settings, largely ignoring their vulnerabilities when faced with flawed premises. Thus, we introduce the **Premise Critique Bench (PCBench)**, designed by incorporating four error types across three difficulty levels, paired with multi-faceted evaluation metrics. We conducted systematic evaluations of 15 representative LLMs, Our findings reveal: (1) Most models rely heavily on explicit prompts to detect errors, with limited autonomous critique; (2) Premise critique ability depends on question difficulty and error type, with direct contradictions being easier to be detected than complex or procedural errors; (3) Reasoning ability does not consistently correlate with the premise critique ability; (4) Flawed premises trigger overthinking in reasoning models, markedly lengthening responses due to repeated attempts at resolving conflicts. These insights underscore the urgent need to enhance LLMs’ proactive evaluation of input validity, positioning premise critique as a foundational capability for developing reliable, human-centric systems.

Don’t Take the Premise for Granted: Evaluating the Premise Critique Ability of Large Language Models

Logic reasoning in natural language has been recognized as an important measure of human intelligence for Large Language Models (LLMs). Popular benchmarks may entangle multiple reasoning skills and thus provide unfaithful evaluations on the logic reasoning skill. Meanwhile, existing logic reasoning benchmarks are limited in language diversity and their distributions are deviated from the distribution of an ideal logic reasoning benchmark, which may lead to biased evaluation results. This paper thereby proposes a new classical logic benchmark DivLogicEval, consisting of natural sentences composed of diverse statements in a counterintuitive way. To ensure a more reliable evaluation, we also introduce a new evaluation metric that mitigates the influence of bias and randomness inherent in LLMs. Through experiments, we demonstrate the extent to which logical reasoning is required to answer the questions in DivLogicEval and compare the performance of different popular LLMs in conducting logical reasoning.

DivLogicEval: A Framework for Benchmarking Logical Reasoning Evaluation in Large Language Models

Text-to-image synthesis has made remarkable progress, yet accurately interpreting complex and lengthy prompts remains challenging, often resulting in semantic inconsistencies and missing details. Existing solutions, such as fine-tuning, are model-specific and require training, while prior automatic prompt optimization (APO) approaches typically lack systematic error analysis and refinement strategies, resulting in limited reliability and effectiveness. Meanwhile, test-time scaling methods operate on fixed prompts and on noise or sample numbers, limiting their interpretability and adaptability. To solve these, we introduce a flexible and efficient test-time prompt optimization strategy that operates directly on the input text. We propose a plug-and-play multi-agent system called GenPilot, integrating error analysis, clustering-based adaptive exploration, fine-grained verification, and a memory module for iterative optimization. Our approach is model-agnostic, interpretable, and well-suited for handling long and complex prompts. Simultaneously, we summarize the common patterns of errors and the refinement strategy, offering more experience and encouraging further exploration. Experiments on DPG-bench and Geneval with improvements of up to 16.9% and 5.7% demonstrate the strong capability of our methods in enhancing the text and image consistency and structural coherence of generated images, revealing the effectiveness of our test-time prompt optimization strategy. The code is available at https://github.com/GenPilot-Any/GenPilot.

GenPilot: A Multi-Agent System for Test-Time Prompt Optimization in Image Generation

Despite their impressive performance in coarse-grained video understanding, Video Large Language Models (Video-LLMs) still face challenges in fine-grained temporal grounding, including ineffective temporal modeling and inadequate timestamp representations. In this work, we introduce Grounded-VideoLLM, a novel Video-LLM designed to perceive and reason over specific video moments with fine-grained temporal precision. Our model features (1) a two-stream encoder that explicitly captures inter-frame relationships while preserving intra-frame visual details and (2) discrete temporal tokens enriched with structured time knowledge for timestamp representation. Besides, we propose a multi-stage training strategy tailored to such grounding-specific architecture. The model is initially trained on simple video-caption tasks and progressively introduced to complex video temporal grounding tasks, ensuring a smooth learning curve and temporal alignment. We further strengthen Grounded-VideoLLM’s temporal reasoning by constructing a VideoQA dataset with grounded information using an automated annotation pipeline. Extensive experiments demonstrate that Grounded-VideoLLM not only surpasses existing models in fine-grained grounding tasks but also exhibits strong potential as a general video understanding assistant.

Grounded-VideoLLM: Sharpening Fine-grained Temporal Grounding in Video Large Language Models

Dongba pictographic is the only pictographic script still in use in the world. Its pictorial ideographic features carry rich cultural and contextual information. However, due to the lack of relevant datasets, research on semantic understanding of Dongba hieroglyphs has progressed slowly. To this end, we constructed DongbaMIE - the first dataset focusing on multimodal information extraction of Dongba pictographs. The dataset consists of images of Dongba hieroglyphic characters and their corresponding semantic annotations in Chinese. It contains 23,530 sentence-level and 2,539 paragraph-level high-quality text-image pairs. The annotations cover four semantic dimensions: object, action, relation and attribute. Systematic evaluation of mainstream multimodal large language models shows that the models are difficult to perform information extraction of Dongba hieroglyphs efficiently under zero-shot and few-shot learning. Although supervised fine-tuning can improve the performance, accurate extraction of complex semantics is still a great challenge at present.

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Next from EMNLP 2025

VehicleWorld: A Highly Integrated Multi-Device Environment for Intelligent Vehicle Interaction

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Towards Tool Use Alignment of Large Language Models