China

The functionality of Large Language Model (LLM) agents is primarily determined by two capabilities: action planning and answer summarization. The former, action planning, is the core capability that dictates an agent&#39;s performance. However, prevailing training paradigms employ end-to-end, multi-objective optimization that jointly trains both capabilities. This paradigm faces two critical challenges: imbalanced optimization objective allocation and scarcity of verifiable data, making it difficult to enhance the agent&#39;s planning capability. To address these challenges, we propose Reinforcement Learning with Tool-use Rewards (RLTR), a novel framework that decouples the training process to enable a focused, single-objective optimization of the planning module. Crucially, RLTR introduces a reward signal based on tool-use completeness to directly evaluate the quality of tool invocation sequences. This method offers a more direct and reliable training signal than assessing the final response content, thereby obviating the need for verifiable data. Our experiments demonstrate that RLTR achieves an 8%–12% improvement in planning performance compared to end-to-end baselines. Moreover, this enhanced planning capability, in turn, translates to a 5%–6% increase in the final response quality of the overall agent system.

EMNLP 2025

Encouraging Good Processes Without the Need for Good Answers: Reinforcement Learning for LLM Agent Planning

agentic rl

agent planning

The functionality of Large Language Model (LLM) agents is primarily determined by two capabilities: action planning and answer summarization. The former, action planning, is the core capability that dictates an agent's performance. However, prevailing training paradigms employ end-to-end, multi-objective optimization that jointly trains both capabilities. This paradigm faces two critical challenges: imbalanced optimization objective allocation and scarcity of verifiable data, making it difficult to enhance the agent's planning capability. To address these challenges, we propose Reinforcement Learning with Tool-use Rewards (RLTR), a novel framework that decouples the training process to enable a focused, single-objective optimization of the planning module. Crucially, RLTR introduces a reward signal based on tool-use completeness to directly evaluate the quality of tool invocation sequences. This method offers a more direct and reliable training signal than assessing the final response content, thereby obviating the need for verifiable data. Our experiments demonstrate that RLTR achieves an 8%–12% improvement in planning performance compared to end-to-end baselines. Moreover, this enhanced planning capability, in turn, translates to a 5%–6% increase in the final response quality of the overall agent system.

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.

Contact centers process millions of customer conversations daily, requiring Quality Assurance (QA) teams to evaluate agent performance against compliance and service standards, often by answering agent evaluation questionnaires. Traditional manual QA cannot scale to growing volumes, while fully automated evaluation using large language models presents a cost-performance trade-off. High-performing models excel at detecting rare but business-critical Answers of Interest (AoI) but incur prohibitive costs, while smaller fine-tuned models are economical but suffer from poor AoI precision, generating high false positive rates that erode agent trust and waste QA resources. We introduce STREAQ, a two-tier selective routing framework to intelligently route queries between cost-efficient and high-capability models. Based on benchmarking on a proprietary dataset across six large LMs, STREAQ achieves substantial cost reduction while preserving critical performance. Using Nova-Pro, STREAQ reduces daily costs by 48% from 34,162 to17,842 while retaining 88.9% of full-model AoI precision. Our ablation studies reveal that flawed reasoning from smaller models can degrade performance, emphasizing the importance of carefully designing routing systems, making enterprise-scale automated QA both practical and economically viable.

STREAQ: Selective Tiered Routing for Effective and Affordable Contact Center Quality Assurance

We present a high-quality, multi-domain dataset for the Text2Cypher task which is enabling the translation of natural language (NL) questions into executable Cypher queries over graph databases. The dataset comprises 27,529 NL queries and corresponding Cyphers spanning across 11 real-world graph datasets, each accompanied by its corresponding graph database for grounded query execution. To ensure correctness, the queries are validated through a rigorous pipeline combining automated schema, runtime and value checks, along with manual review for logical correctness. Queries are further categorized by complexity to support fine-grained evaluation. We have released our benchmark dataset and code to replicate our data synthesis pipeline on new graph datasets, supporting extensibility and future research for the task of Text2Cypher.

Mind the Query: A Benchmark Dataset towards Text2Cypher Task

Supervised fine-tuning (SFT) on benign data can paradoxically erode a language model’s safety alignment, a phenomenon known as catastrophic forgetting of safety behaviors. Although prior work shows that randomly adding safety examples can reduce harmful output, the principles that make certain examples more effective than others remain poorly understood. This paper investigates the hypothesis that the effectiveness of a safety example is governed by two key factors: its instruction-response behavior (e.g., refusal vs. explanation) and its semantic diversity across harm categories. We systematically evaluate sampling strategies based on these axes and find that structured, diversity-aware sampling significantly improves model safety. Our method reduces harmfulness by up to 41% while adding only 0.05% more data to the fine-tuning set.

How to Fine-Tune Safely on a Budget: Model Adaptation Using Minimal Resources

Large language models (LLMs) have demonstrated impressive capabilities across diverse tasks, but they remain susceptible to hallucinations—generating content that appears plausible but contains factual inaccuracies. We present Finch-Zk, a black-box framework that leverages fine-grained cross-model consistency to detect and mitigate hallucinations in LLM outputs without requiring external knowledge sources. Finch-Zk introduces two key innovations: 1) a cross-model consistency checking strategy that reveals fine-grained inaccuracies by comparing responses generated by diverse models from semantically-equivalent prompts, and 2) a targeted mitigation technique that applies precise corrections to problematic segments while preserving accurate content. Experiments on the FELM dataset show Finch-Zk improves hallucination detection F1 scores by 6-39\% compared to existing approaches. For mitigation, Finch-Zk achieves 7-8 absolute percentage points improvement in answer accuracy on the GPQA-diamond dataset when applied to state-of-the-art models like Llama 4 Maverick and Claude 4 Sonnet. Extensive evaluation across multiple models demonstrates that Finch-Zk provides a practical, deployment-ready safeguard for enhancing factual reliability in production LLM systems.

Zero-knowledge LLM hallucination detection and mitigation through fine-grained cross-model consistency

Identifying attribute values from product profiles is a key task for improving product search, recommendation, and business analytics on e-commerce platforms, which we called Product Attribute Value Identification (PAVI) . However, existing PAVI methods face critical challenges, such as cascading errors, inability to handle out-of-distribution (OOD) attribute values, and lack of generalization capability. To address these limitations, we introduce Multi-Value-Product Retrieval-Augmented Generation (MVP-RAG), combining the strengths of retrieval, generation, and classification paradigms. MVP-RAG defines PAVI as a retrieval-generation task, where the product title description serves as the query, and products and attribute values act as the corpus. It first retrieves similar products of the same category and candidate attribute values, and then generates the standardized attribute values. The key advantages of this work are: (1) the proposal of a multi-level retrieval scheme, with products and attribute values as distinct hierarchical levels in PAVI domain (2) attribute value generation of large language model to significantly alleviate the OOD problem and (3) its successful deployment in a real-world industrial environment. Extensive experimental results on the dataset demonstrate that the proposed method performs better than the state-of-the-art baselines.

Multi-Value-Product Retrieval-Augmented Generation for Industrial Product Attribute Value Identification

We introduce DispatchQA, a benchmark to evaluate how well small language models (SLMs) translate open‑ended search queries into executable API calls via explicit function calling. Our benchmark focuses on the latency-sensitive e-commerce setting and measures SLMs' impact on both search relevance and search latency. We provide strong, replicable baselines based on Llama 3.1 8B Instruct fine-tuned on synthetically generated data and find that fine-tuned SLMs produce search quality comparable or better than large language models such as GPT-4o while achieving up to 3× faster inference. All data, code, and training checkpoints are publicly released to spur further research on resource‑efficient query understanding.

DispatchQA: A Benchmark for Small Function Calling Language Models in E-Commerce Applications

This paper explores effective strategies for persuasive dialogue agents. Current approaches often rely on a limited set of predefined strategies, failing to capture the complexity of real-world persuasive interactions. 
To create more practically relevant strategies, we propose a cross-disciplinary approach drawing on social psychology, behavioral economics, and communication theory.
We validate our method through experiments on two distinct datasets: P4G, representing a specific, in-domain scenario, and DailyPersuasion, covering a wide variety of scenarios. Our approach achieves strong results across both of these datasets, demonstrating a significant improvement in persuasion success rates and suggesting promising generalizability. Notably, our method also excels in persuading individuals with initially low intent, addressing a critical challenge in persuasive AI.

Enhancing Persuasive Dialogue Agents by Synthesizing Cross‑Disciplinary Communication Strategies

Large Language Models (LLMs) often generate incorrect or outdated information, especially in low-resource settings or when dealing with private data. To address this, Retrieval-Augmented Generation (RAG) uses external knowledge bases (KBs), but these can also suffer from inaccuracies. We introduce STACKFEED, a novel Structured Textual Actor-Critic Knowledge base editing with Feedback approach that iteratively refines the KB based on expert feedback using a multi-actor, centralized critic reinforcement learning framework. STACKFEED defines a ReACT actor agent on each document to perform structured edits based on document-specific targeted instructions. Experimental results showcase that STACKFEED significantly improves KB quality and performance of the RAG system. We evaluate STACKFEED on low-resource programming problems, modified Python packages, and factual question-answering tasks.

STACKFEED: Structured Textual Actor-Critic Knowledge base editing with FEEDback

High-quality doctor-patient dialogues are crucial for accurate Electronic Medical Record (EMR) generation. However, collecting large-scale real dialogues is costly and constrained by privacy regulations, while existing synthetic methods often yield rigid and medically inconsistent dialogues. We propose a scalable framework integrating (1) Intent Graph Planning for diverse clinical flows, (2) Dual-Agent Simulation for realistic doctor-patient interactions, and (3) Rule-Reward Quality Control combining explicit medical rules with a self-supervised reward model. Experiments across multiple clinical domains demonstrate that our synthesized dialogues significantly enhance realism, diversity, and downstream EMR quality, substantially reducing physician editing efforts. Our framework provides a practical and privacy-compliant solution for deploying robust clinical NLP systems.

High-Quality Medical Dialogue Synthesis for Improving EMR Generation

Large Language Models (LLMs) can achieve enhanced complex problem-solving through test-time computing scaling, yet this often entails longer contexts and numerous reasoning token costs. 
In this paper, we propose an efficient test-time scaling method that trains LLMs on code-related reasoning trajectories, facilitating their reduction of excess thinking tokens while maintaining performance.
First, we create Z1-Code-Reasoning-107K, a curated dataset of simple and complex coding problems paired with their short and long solution trajectories. 
Second, we present a novel Shifted Thinking Window to mitigate overthinking overhead by removing context-delimiting tags (e.g., <think>…</think>) and capping reasoning tokens. 
Trained with long and short trajectory data and equipped with Shifted Thinking Window, our model, Z1-7B, demonstrates the ability to adjust its reasoning level as the complexity of problems and exhibits efficient test-time scaling across different reasoning tasks that matches R1-Distill-Qwen-7B performance with about 30\% of its average thinking tokens.
Notably, fine-tuned with only code trajectories, Z1-7B demonstrates generalization to broader reasoning tasks (47.5\% on GPQA Diamond). 
Our analysis of efficient reasoning elicitation also provides valuable insights for future research.

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