China

Evaluation and Management (E/M) coding, under the Current Procedural Terminology (CPT) taxonomy, documents medical services provided to patients by physicians. Used primarily for billing purposes, it is in physicians&#39; best interest to provide accurate CPT E/M codes. Automating this coding task will help alleviate physicians&#39; documentation burden, improve billing efficiency, and ultimately enable better patient care. However, a number of real-world complexities have made E/M encoding automation a challenging task. In this paper, we elaborate some of the key complexities and present ProFees, our LLM-based framework that tackles them, followed by a systematic evaluation. On an expert-curated real-world dataset, ProFees achieves an increase in coding accuracy of more than 36% over a commercial CPT E/M coding system and almost 5% over our strongest single-prompt baseline, demonstrating its effectiveness in addressing the real-world complexities.

EMNLP 2025

Taming the Real-world Complexities in CPT E/M Coding with Large Language Models

Evaluation and Management (E/M) coding, under the Current Procedural Terminology (CPT) taxonomy, documents medical services provided to patients by physicians. Used primarily for billing purposes, it is in physicians' best interest to provide accurate CPT E/M codes. Automating this coding task will help alleviate physicians' documentation burden, improve billing efficiency, and ultimately enable better patient care. However, a number of real-world complexities have made E/M encoding automation a challenging task. In this paper, we elaborate some of the key complexities and present ProFees, our LLM-based framework that tackles them, followed by a systematic evaluation. On an expert-curated real-world dataset, ProFees achieves an increase in coding accuracy of more than 36% over a commercial CPT E/M coding system and almost 5% over our strongest single-prompt baseline, demonstrating its effectiveness in addressing the real-world complexities.

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.

We report on experiments on information extraction (IE) from EU Acquis, the European Union law. We introduce a new IE task of Information Provision Activity Requirement Extraction, which focuses on the identification of text fragments which introduce an obligation to provide information and extraction of structured information therefrom on the key entities involved and temporal modalities. We compare various technologies for this task, including, knowledge-, classical ML-, transformer-, and generative AI-based approaches on a benchmark corpus specifically created for this study.

Extraction of Information Provision Activity Requirements from EU-Acquis

Knowledge graphs (KGs) enhance pretrained language models by incorporating additional knowledge, improving their performance in specialized fields, for example, helping models learn domain-specific relationships between documents that might otherwise be missed. In the process industry, text logs contain crucial information about daily operations, such as events, instructions, and incident reports, and are often structured as sparse KGs. This paper explores how SciNCL, a graph-aware neighborhood contrastive learning methodology originally designed for scientific publications, can be adapted to the process industry domain. We use several KGs to train graph embedding (GE) models, which we then use to generate synthetic training datasets for a domain-specific text encoder. Our experiments demonstrate that language models fine-tuned with triplets derived from GE outperform a state-of-the-art mE5-large text encoder by 12-13.5% (6.68-7.54p) on the proprietary process industry text embedding benchmark (PITEB) while being 3-5 times smaller in size.

Contrastive Learning Using Graph Embeddings for Domain Adaptation of Language Models in the Process Industry

Large language models usually suffer from multiple-file coding scenarios where strong inter-file dependencies manifest, typically demonstrated in SWE-bench. To mitigate this issue, we propose Think-Search-Patch (TSP), a retrieval-augmented reasoning framework for repository-level code repair. In the Think stage, our system breaks down a coding task and creates clear search query. Next, in the Search stage, it retrieves relevant code snippets using models like E5. In the final Patch stage, it generates standardized patches based on the key snippets. In addition the proposed framework, we enhance system reliability through a two-stage training process. In the first stage, the system undergoes supervised fine-tuning (SFT) on our TSP dataset. In the subsequent stage, we employ rejection sampling with correction to generate preference pairs for Direct Preference Optimization (DPO) training, thereby reducing errors in the intermediate phases. Experimental results demonstrate that TSP framework enhances retrieval accuracy and repair success on SWE-bench Lite, even surpassing models with a larger size in managing extensive code contexts and successfully addressing bugs spanning across multiple files. All data and code will be available soon at GitHub.

Think-Search-Patch: A Retrieval-Augmented Reasoning Framework for Repository-Level Code Repair

LLM agents show promise for vulnerability testing. We however lack benchmarks to evaluate and compare solutions. AutoPenBench covers this need offering an open benchmark for the evaluation of vulnerability testing agents. It includes 33 tasks, ranging from introductory exercises to actual vulnerable systems. It supports MCP, enabling the comparison of agent capabilities. We introduce milestones per task, allowing the comparison of intermediate steps where agents struggle. To illustrate the use of AutoPenBench we evaluate autonomous and human-assisted agent architectures. The former achieves 21\% success rates, insufficient for production, while human-assisted agents reach 64\% success, indicating a viable industrial path. AutoPenBench is offered as open source and enables fair comparison of agents.

AutoPenBench: A Vulnerability Testing Benchmark for Generative Agents

In enterprise systems, tasks like API integration, ETL pipeline creation, customer record merging, and data consolidation rely on accurately aligning attributes that refer to the same real-world concept but differ across schemas. This semantic attribute alignment is critical for enabling schema unification, reporting, and analytics. The challenge is amplified in schema only settings where no instance data is available due to ambiguous names, inconsistent descriptions, and varied naming conventions.


We propose a hybrid, unsupervised framework that combines the contextual reasoning of Large Language Models (LLMs) with the stability of embedding-based similarity and schema grouping to address token limitations and hallucinations. Our method operates solely on metadata and scales to large schemas by grouping attributes and refining LLM outputs through embedding-based enhancement, justification filtering, and ranking. Experiments on real-world healthcare schemas show strong performance, highlighting the effectiveness of the framework in privacy-constrained scenarios.

Group, Embed and Reason: A Hybrid LLM and Embedding Framework for Semantic Attribute Alignment

Safety is a paramount concern in clinical chatbot applications, where inaccurate or harmful responses can lead to serious consequences. Existing methods—such as guardrails and tool-calling—often fall short in addressing the nuanced demands of the clinical domain. In this paper, we introduce TACOS(Taxonomy of Comprehensive Safety for Clinical Agents), a fine-grained, 21-class taxonomy that integrates safety filtering and tool selection into a single user intent classification step. TACOS covers a wide spectrum of clinical and non-clinical queries, explicitly modeling varying safety thresholds and external tool dependencies. To validate our framework, we curate a TACOS-annotated dataset and perform extensive experiments. Our results demonstrate the value of a new taxonomy specialized for clinical agent settings, and reveal valuable insights about train data distribution and pretrained knowledge of base models.

Taxonomy of Comprehensive Safety for Clinical Agents

Text-based telemedicine has become increasingly common, yet the quality of medical advice in doctor-patient interactions is often judged more on how advice is communicated rather than its clinical accuracy. To address this, we introduce Dr.Copilot, a multi-agent large language model (LLM) system that supports Romanian-speaking doctors by evaluating and enhancing the presentation quality of their written responses. Rather than assessing medical correctness, Dr.Copilot provides feedback along 17 interpretable axes. The system comprises of three LLM agents with prompts automatically optimized via DSPy. Designed with low-resource Romanian data and deployed using open-weight models, it delivers real-time specific feedback to doctors within a telemedicine platform. Empirical evaluations and live deployment with 41 doctors show measurable improvements in user reviews and response quality, marking one of the first real-world deployments of LLMs in Romanian medical settings.

Dr. Copilot: A Multi-Agent Prompt Optimized Assistant for Improving Patient-Doctor Communication in Romanian

We present CLARITY (Clinical Assistant for Routing, Inference, and Triage), an AI-driven platform designed to facilitate patient-to-specialist routing, clinical consultations, and severity assessment of patients' conditions. Its hybrid architecture combines a Finite State Machine (FSM) for structured dialogue flows with collaborative agents that employ Large Language Model (LLM) to analyze symptoms and prioritize referrals to appropriate specialists. Built on a modular microservices framework, CLARITY ensures safe, efficient, and robust performance, flexible and readily scalable to meet the demands of existing workflows and IT solutions in healthcare.


We report integration of our clinical assistant into a large-scale nation-wide inter-hospital IT platform, with over 55,000 content-rich user dialogues completed within the two months of deployment, 2,500 of which were expert-annotated for a consequent validation. The validation results show that CLARITY surpasses human-level performance in terms of the first-attempt routing precision, naturally requiring up to 3 times shorter duration of the consultation than with a human.

CLARITY: Clinical Assistant for Routing, Inference, and Triage

The lack of high-quality test collections challenges Information Retrieval (IR) in specialized domains. This work addresses this issue by comparing supervised classifiers against zero-shot Large Language Models (LLMs) for automated relevance annotation in the oil and gas industry, using human expert judgments as a benchmark. A supervised classifier, trained on limited expert data, outperforms LLMs, achieving an F1-score that surpasses even a second human annotator. The study also empirically confirms that LLMs are susceptible to unfairly prefer technologically similar retrieval systems. While LLMs lack precision in this context, a well-engineered classifier offers an accurate and practical path to scaling evaluation datasets within a human-in-the-loop framework that empowers, not replaces, human expertise.

Analysis of Automated Document Relevance Annotation for Information Retrieval in Oil and Gas Industry

Classifying contact center interactions into a large number of categories is critical for downstream analytics, but challenging due to high label cardinality, and cost constraints. While Large Language Models (LLMs) offer flexibility for such tasks, existing methods degrade with increasing label space, showing significant inconsistencies and sensitivity to label ordering. We propose a scalable, cost-effective two-step retrieval-augmented classification framework, enhanced with a multi-view representation of labels. Our method significantly improves accuracy and consistency over baseline LLM approaches. Experiments across 4 private and 5 open datasets yield performance improvements of upto 14.6% while reducing inference cost by 60-91% compared to baseline approaches.


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