China

Modern language models are evaluated on large benchmarks. Given how many different numbers these evaluations output, making sense of them for model selection can be difficult. We take a closer look at this using a model-centric lens and look at the evaluation numbers themselves. In this work, we analyze benchmarks in three stages: dataset &amp; model comparison, representative set identification, and performance prediction. Since datasets and models relate strongly to one another, we develop an algorithm to identify a representative set of datasets that covers a benchmark using the raw evaluation scores alone. Using our algorithm, we find that with 5.9% (1/17), 1.7% (1/58), and 16.2% (12/74) of the datasets for HELM, MMLU, and BigBenchLite respectively, we achieve coverage levels of at least 95%. Additionally, using just these representative subsets, we can both preserve model ranks and predict performance on a held-out set of models with near zero mean-squared error. Taken together, our analysis can help model developers improve efficiency and allow dataset creators validate whether their newly created dataset differs from existing datasets in the benchmark.

EMNLP 2025

SimBA: Simplifying Benchmark Analysis Using Performance Matrices Alone

benchmarks

efficiency

evaluation

Modern language models are evaluated on large benchmarks. Given how many different numbers these evaluations output, making sense of them for model selection can be difficult. We take a closer look at this using a model-centric lens and look at the evaluation numbers themselves. In this work, we analyze benchmarks in three stages: dataset & model comparison, representative set identification, and performance prediction. Since datasets and models relate strongly to one another, we develop an algorithm to identify a representative set of datasets that covers a benchmark using the raw evaluation scores alone. Using our algorithm, we find that with 5.9% (1/17), 1.7% (1/58), and 16.2% (12/74) of the datasets for HELM, MMLU, and BigBenchLite respectively, we achieve coverage levels of at least 95%. Additionally, using just these representative subsets, we can both preserve model ranks and predict performance on a held-out set of models with near zero mean-squared error. Taken together, our analysis can help model developers improve efficiency and allow dataset creators validate whether their newly created dataset differs from existing datasets in the benchmark.

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.

Text embeddings play an important role in NLP but are costly to store and use. Compressing embeddings addresses these challenges, but selecting the best compression methods remains difficult. Existing evaluation methods for compressed embeddings are either expensive or too simplistic. We introduce a new intrinsic evaluation framework with multiple task-agnostic metrics, including a novel spectral fidelity measure called \textbf{EOS } that is resilient to embedding anisotropy. We tested on a set of embeddings across four tasks. Our framework shows that intrinsic metrics reliably predict downstream performance and reveal how different models rely on local versus global structure. This provides a practical, efficient, and interpretable alternative to standard evaluations for compressed embeddings\footnote{We will release the framework to the public. This will save researchers significant time.}.

Do We Really Need All Those Dimensions? An Intrinsic Evaluation Framework for Compressed Embeddings

Table understanding is a crucial task in document processing and is commonly encountered in practical applications. We introduce 2Columns1Row, the first open-source benchmark for the table question answering task in Russian. This benchmark evaluates the ability of models to reason about the relationships between rows and columns in tables, employing both textual and multimodal inputs. 2Columns1Row consists of six datasets, 28,680 tables, designed datasets that vary in the complexity of the text within the table contents and the consistency of the values in the cells. We evaluate the models using text-only and multimodal approaches and analyze their performance. Through extensive evaluation, we demonstrate the limitations of current multimodal models on this task and prove the feasibility of a dynamic text-based system utilizing our benchmark. Our results highlight significant opportunities for advancing table understanding and reasoning, providing a solid foundation for future research in this domain.

2Columns1Row: A Russian Benchmark for Textual and Multimodal Table Understanding and Reasoning

Large-scale generative models like DeepSeek-R1 and OpenAI-O1 benefit substantially from chain-of-thought (CoT) reasoning, yet pushing their performance typically requires vast data, large model sizes, and full-parameter fine-tuning. While parameter‐efficient fine‐tuning (PEFT) helps reduce cost, most existing approaches primarily address domain adaptation or layer‐wise allocation rather than explicitly tailoring data and parameters to different response demands. Inspired by “Thinking, Fast and Slow,” which characterizes two distinct modes of thought—System 1 (fast, intuitive, often automatic) and System 2 (slower, more deliberative and analytic)—we draw an analogy that different “subregions” of an LLM’s parameters might similarly specialize for tasks that demand quick, intuitive responses versus those requiring multi-step logical reasoning. Therefore, we propose LoRA-PAR, a dual-system LoRA framework that partitions both data and parameters by System 1 or System 2 demands, using fewer yet more focused parameters for each task. Specifically, we classify task data via multi-model role-playing and voting, and partition parameters based on importance scoring, then adopt a two-stage fine-tuning strategy of training System 1 tasks with supervised fine-tuning (SFT) to enhance knowledge and intuition and refine System 2 tasks with reinforcement learning (RL) to reinforce deeper logical deliberation next. Extensive experiments show that the two-stage fine-tuning strategy, SFT and RL, lowers active parameter usage while matching or surpassing SOTA PEFT baselines.

LoRA-PAR: A Flexible Dual-System LoRA Partitioning Approach to Efficient LLM Fine-Tuning

Existing pretraining data mixing methods for large language models (LLMs) typically follow a domain-wise methodology, a top-down process that first determines domain weights and then performs uniform data sampling across each domain. However, these approaches neglect significant inter-domain overlaps and commonalities, failing to control the global diversity of the constructed training dataset. Further, uniform sampling within domains ignores fine-grained sample-specific features, potentially leading to suboptimal data distribution. To address these shortcomings, we propose a novel sample-wise data mixture approach based on a bottom-up paradigm. This method performs global cross-domain sampling by systematically evaluating the quality and diversity of each sample, thereby dynamically determining the optimal domain distribution. Comprehensive experiments across multiple downstream tasks and perplexity assessments demonstrate that SampleMix surpasses existing domain-based methods. Meanwhile, SampleMix requires 1.4x to 2.1x fewer training steps to achieve the baselines’ performance, highlighting the substantial potential of SampleMix to optimize pre-training data. The code and data are available at https://anonymous.4open.science/r/SampleMix-910C, and can also be found in the Software part of ARR page.

SampleMix: A Sample-wise Pre-training Data Mixing Strategy by Coordinating Data Quality and Diversity

Test-time scaling large language models (LLMs), such as DeepSeek-R1 and OpenAI's o1, enhances reasoning by extending inference-time chain-of-thought traces. However, their legal reasoning capabilities remain underexplored. We conduct the first systematic evaluation of 10 LLMs --- including both reasoning and general-purpose models --- across 17 Chinese and English legal benchmarks covering statutory and case-law traditions. To bridge the domain gap, we curate a chain-of-thought-annotated legal corpus and train Legal-R1-14B, an open-source legal specialist model. Legal-R1-14B outperforms both o1-preview and DeepSeek-R1 on several benchmarks, establishing a new baseline for legal reasoning. Error analysis reveals ongoing challenges such as outdated legal knowledge, reasoning failures, and factual hallucinations, highlighting key directions for future work in legal-domain LLMs.

Evaluating Test-Time Scaling LLMs for Legal Reasoning: OpenAI o1, DeepSeek-R1, and Beyond

A conflict of interest (COI) appears when a person or a company has two or more interests that may directly conflict. This happens, for instance, when a scientist whose research is funded by a company audits the same company. For transparency and to avoid undue influence, public repositories of relations of interest are becoming recommended or mandatory in various areas, and can be used to avoid COIs. In this work, we propose an LLM-based open information extraction (OpenIE) framework for extracting financial or other types of interesting relations from scientific text. We target scientific publications in which authors declared funding sources or collaborations in the acknowledgment section, or in the metadata, or in the publication, following editors’ requirements. We propose an extraction methodology, an evaluation methodology, and a taxonomy of relations. Finally, we perform a comparative study of disclosures in two journals in the field of toxicology and pharmacology.

The Search for Conflicts of Interest: Open Information Extraction in Scientific Publications

In specialized domains such as space science and utilization, question answering (QA) systems are required to perform complex multi-fact reasoning over sparse knowledge graphs (KGs). Existing KG-based retrieval-augmented generation (RAG) frameworks often face challenges such as inefficient subgraph retrieval, limited reasoning capabilities, and high computational costs. These issues limit their effectiveness in specialized domains. In this paper, we propose SKRAG, a novel Skeleton-guided RAG framework for knowledge graph question answering (KGQA). SKRAG leverages a lightweight language model enhanced with the Finite State Machine (FSM) constraint to produce structurally grounded reasoning skeletons, which guide accurate subgraph retrieval. The retrieved subgraph is then used to prompt a general large language model (LLM) for answer generation. We also introduce SSUQA, a KGQA dataset in the space science and utilization domain. Experiments show that SKRAG outperforms strong baselines on SSUQA and two general-domain benchmarks, demonstrating its adaptability and practical effectiveness.

SKRAG: A Retrieval-Augmented Generation Framework Guided by Reasoning Skeletons over Knowledge Graphs

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.

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

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.

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Do We Really Need All Those Dimensions? An Intrinsic Evaluation Framework for Compressed Embeddings

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