China

Multi-agent systems (MAS) powered by large language models (LLMs) have shown potential in tackling multifaceted problems through advanced understanding and reasoning. However, they struggle to adapt to evolving task dependencies and to handle uncertainties, such as shifting priorities or unpredictable disruptions. These constraints undermine their ability to dynamically adjust long-term strategies and inter-agent collaboration. To address these challenges, we propose DeMAC, a Dynamic Environment-Aware Manager-Player Agents Coordination framework that enhances multi-agent coordination through long-term strategic planning. DeMAC uses a dynamically updated directed acyclic graph (DAG) and a Manager-Player Dual-Feedback mechanism to align strategic and operational decisions. Moreover, DeMAC enables agents to maintain collaboration and dynamically adapt to changing environmental conditions, outperforming traditional reinforcement learning and human-agent collaboration in the Overcooked simulation. Experimental results highlight DeMAC’s ability to tackle complex coordination tasks, demonstrating its potential to advance LLM-based MAS in dynamic, complex task dependency environments.

EMNLP 2025

DeMAC: Enhancing Multi-Agent Coordination with Dynamic DAG and Manager-Player Feedback

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."<br>

*Message from the General Chair, Dirk Hovy*

[**Link to Conference Handbook**](https://drive.google.com/file/d/1johU5QqVVYO4RfH7QcIORr7qrVBdzdwC/view?usp=sharing)





<br>

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

Dialects exhibit a substantial degree of lexical variation due to the lack of a standard orthography. At the same time, Large Language Models’ (LLMs) ability to process dialects remains largely understudied. To address this gap, we conduct a fine-grained analysis of dialect variation across different parts-of-speech. Using Bavarian as a case study, we investigate the lexical dialect understanding capability of LLMs by examining how they recognize and translate dialectal terms. To this end, we introduce DiaLemma, a novel annotation framework for obtaining dialect variation dictionaries from monolingual data only, and use it to create a ground truth dataset of 100K human-annotated German-Bavarian word pairs. We evaluate how well nine state-of-the-art LLMs can recognize Bavarian terms as dialect translations, inflected variants, or unrelated forms of a given German lemma. Our evaluation reveals that LLMs are better at translating and recognizing nouns. Surprisingly, when used as dialect word translation models, we find that providing additional context in the form of example usages can boost their performance. Our results highlight the limitations of LLMs in dealing with orthographic dialect variation and emphasizes the need for future work on adapting LLMs to dialects.

Make Every Letter Count: Building Dialect Variation Dictionaries from Monolingual Corpora

As machine learning (ML) application continues to expand across diverse fields, there is a rising demand for ML code generation. In this paper, we aim at a critical research question: Can machines autonomously generate ML code for sophisticated, human-designed algorithms or solutions? To answer this question, we introduce a novel benchmark, MLAlgo-Bench, which includes two challenging tasks: 1) Generating code for ML algorithms including both traditional ML and modern deep learning-based methods, and 2) Giving humans solution sketches, writing ML code for solving practical tasks in Kaggle competitions. This benchmark is unique in its focus on the challenges of interpreting intricate human instructions and producing multi-step, high-complexity code, offering a rigorous test for current Large Language Model (LLM) capabilities. We introduce an automatic evaluation framework with comprehensive metrics such as task pass rate, relative performance metric, and time overhead. Currently, the top-performing models (Claude3.5-Sonet) achieve a 48.8% task completion rate on realizing machine learning algorithms, and a 21.6% rate for completing Kaggle competitions. Further analysis suggests substantial room for improvement.

MLAlgo-Bench: Can Machines Implement Machine Learning Algorithms?

Minecraft, as an open-world virtual interactive environment, has become a prominent platform for research on agent decision-making and execution. Existing works primarily adopt a single Large Language Model (LLM) agent to complete various in-game tasks. However, for complex tasks requiring lengthy sequences of actions, single-agent approaches often face challenges related to inefficiency and limited fault tolerance. Despite these issues, research on multi-agent collaboration remains scarce. In this paper, we propose CausalMACE, a holistic causality planning framework designed to enhance multi-agent systems, in which we incorporate causality to manage dependencies among subtasks. Technically, our proposed framework introduces two modules: an overarching task graph for global task planning and a causality-based module for dependency management, where inherent rules are adopted to perform causal intervention. Experimental results demonstrate our approach achieves state-of-the-art performance in multi-agent cooperative tasks of Minecraft. The code will be open-sourced upon the acceptance of this paper.

CausalMACE: Causality Empowered Multi-Agents in Minecraft Cooperative Tasks

LLM training is resource-intensive. Quantized training improves computational and memory efficiency but introduces quantization noise, which can hinder convergence and degrade model accuracy. Stochastic Rounding (SR) has emerged as a theoretically attractive alternative to deterministic rounding, offering unbiased gradient estimates. However, its interaction with other training factors—especially batch size—remains underexplored. In this paper, we present a theoretical and empirical study of mini-batch stochastic gradient descent (SGD) with SR, showing that increased batch sizes can compensate for reduced precision during backpropagation. Furthermore, we show that quantizing weights and activations impacts gradient variance in distinct ways. Our experiments validate these theoretical insights. Our experiments validate these theoretical insights.

Training with Fewer Bits: Unlocking Edge LLMs Training with Stochastic Rounding

Large language models (LLMs) have achieved remarkable success in generative tasks, yet they often fall short in ensuring the factual accuracy of their outputs this limiting their reliability in real-world applications where correctness is critical. In this paper, we present FactReasoner, a novel neuro-symbolic based factuality assessment framework that employs probabilistic reasoning to evaluate the truthfulness of long-form generated responses. FactReasoner decomposes a response into atomic units, retrieves relevant contextual information from external knowledge sources, and models the logical relationships (e.g., entailment, contradiction) between these units and their contexts using probabilistic encodings. It then estimates the posterior probability that each atomic unit is supported by the retrieved evidence. Our experiments on both labeled and unlabeled benchmark datasets demonstrate that FactReasoner often outperforms state-of-the-art prompt-based methods in terms of factual precision and recall.

FactReasoner: A Probabilistic Approach to Long-Form Factuality Assessment for Large Language Models

The rapid advancements in large language models (LLMs) have led to the emergence of routing techniques, which aim to efficiently select the optimal LLM from diverse candidates to tackle specific tasks, optimizing performance while reducing costs. Current LLM routing methods are limited in effectiveness due to insufficient exploration of the intrinsic connection between user queries and the characteristics of LLMs. To address this issue, in this paper, we present **RadialRouter**, a novel framework for LLM routing which employs a lightweight Transformer-based backbone with a radial structure named **RadialFormer** to articulate the query-LLMs relationship. The optimal LLM selection is performed based on the final states of RadialFormer. The pipeline is further refined by an objective function that combines Kullback-Leibler divergence with the query-query contrastive loss to enhance robustness. Experimental results on RouterBench show that RadialRouter significantly outperforms existing routing methods by 9.2\% and 5.8\% in the *Balance* and *Cost First* scenarios, respectively. Additionally, its adaptability toward different performance-cost trade-offs and the dynamic LLM pool demonstrates practical application potential.

RadialRouter: Structured Representation for Efficient and Robust Large Language Models Routing

Multi-modal Dialogue Summarization (MDS) is an important task with great applications. To develop and improve the MDS model, a strong automatic evaluation method for MDS could save a lot of money and time. However, a meta-evaluation benchmark with human annotation is a foundation for developing and improving the automatic evaluation methods of MDS. The shortage of such a benchmark motivates us to introduce MDSEval, the first meta-evaluation benchmark for MDS, providing data-summary pairs and human annotation on summary quality across 8 aspects. Besides the novel benchmark dataset, we propose a novel filtering framework based on Mutually Exclusive Key Information (MEKI) across modalities used in enhancing our data quality. Further, our work is the first to define key evaluation aspects for MDS tasks. Our findings reveal that current multi-modal evaluation methods struggle to fairly rate summaries generated by advanced MLLMs. Our datasets, filtering methods, defined evaluation aspects, and findings will greatly benefit the development of MDS evaluation methods

MDSEval: A Meta-Evaluation Benchmark for Multimodal Dialogue Summarization

While large language models (LLMs) have demonstrated impressive creative capabilities, most research has predominantly focused on English texts, often overlooking non-English literary traditions and lacking standardized methods for assessing creativity. In this paper, we investigate the ability of various LLMs to comprehend and generate Persian literary text with culturally relevant expressions. To this end, we construct a dataset of user-generated Persian literary content spanning 20 diverse topics. We evaluate model outputs based on four key dimensions: originality, fluency, flexibility, and elaboration by adapting the Torrance Tests of Creative Thinking to the Persian cultural context. Furthermore, we assess the models’ understanding of four fundamental literary devices: simile, metaphor, hyperbole, and antithesis. Combining human judgment with an LLM-as-judge approach, we evaluate the creativity of the generated texts. Our analysis reveals a strong agreement between human and model assessments, highlighting the potential of LLMs to meaningfully engage with Persian literary culture. However, these models still require further refinement to fully grasp and interpret literary devices.

Evaluating the Creativity of LLMs in Persian Literary Text Generation

Emotion context sensitivity—the ability to adjust emotional responses based on contexts—is a core component of human emotional intelligence. For example, being told, "You can come with me if you want," may elicit joy if the destination is a mall, but provoke fear if the destination is a trap house. As large language models (LLMs) are increasingly deployed in socially interactive settings, understanding this human ability becomes crucial for generating context-appropriate, emotion-aware responses. In this work, we introduce Trace, a novel benchmark for evaluating whether LLMs can understand emotion context sensitivity of humans. This benchmark consists of 1,626 social scenarios and comprises two complementary tests: a sensitivity test, which measures whether models can detect emotional shifts caused by context changes, and a robustness test, which evaluates whether models can maintain stable emotion predictions when context changes are emotionally irrelevant. Each scenario pair keeps the core event constant while systematically varying contextual details—time, place, or agent—based on insights from behavioral theory and emotion psychology. Experimental results show that even the best-performing LLMs lag behind human performance by 20% in the sensitivity test and 15% in the robustness test, indicating substantial room for improvement in emotion-aware reasoning.

"Going to a trap house" conveys more fear than "Going to a mall": Benchmarking Emotion Context Sensitivity for LLMs

Large language models (LLMs) have shown impressive performance by generating reasoning paths before final answers, but learning such a reasoning path requires costly human supervision. To address this issue, recent studies have explored self-training methods that improve reasoning capabilities using pseudo-labels generated by the LLMs themselves. Among these, confidence-based self-training fine-tunes LLMs to prefer reasoning paths with high-confidence answers, where confidence is estimated via majority voting. However, such methods exclusively focus on the quality of the final answer and may ignore the quality of the reasoning paths, as even an incorrect reasoning path leads to a correct answer by chance. Instead, we advocate the use of reasoning-level confidence to identify high-quality reasoning paths for self-training, supported by our empirical observations. We then propose a new self-training method, **CORE-PO**, that fine-tunes LLMs to prefer high-**CO**nfidence **RE**asoning paths through **P**olicy **O**ptimization. Our experiments show that CORE-PO improves the accuracy of outputs on four in-distribution and two out-of-distribution benchmarks, compared to existing self-training methods.

Downloads

Next from EMNLP 2025

Make Every Letter Count: Building Dialect Variation Dictionaries from Monolingual Corpora

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES