China

Existing metrics often lack the granularity and interpretability to capture nuanced clinical differences between candidate and ground-truth radiology reports, resulting in suboptimal evaluation. We introduce a **Cl**inically-grounded tabular framework with **E**xpert-curated labels and **A**ttribute-level comparison for **R**adiology report evaluation (**CLEAR**). CLEAR not only examines whether a report can accurately identify the presence or absence of medical conditions, but also assesses whether it can precisely describe each positively identified condition across five key attributes: first occurrence, change, severity, descriptive location, and recommendation. Compared to prior works, CLEAR’s multi-dimensional, attribute-level outputs enable a more comprehensive and clinically interpretable evaluation of report quality. Additionally, to measure the clinical alignment of CLEAR, we collaborate with five board-certified radiologists to develop **CLEAR-Bench**, a dataset of 100 chest X-ray reports from MIMIC-CXR, annotated across 6 curated attributes and 13 CheXpert conditions. Our experiments show that CLEAR achieves high accuracy in extracting clinical attributes and provides automated metrics that are strongly aligned with clinical judgment.

EMNLP 2025

CLEAR: A Clinically Grounded Tabular Framework for Radiology Report Evaluation

nlp datasets

automatic evaluation of datasets

evaluation methodologies

## 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*





<br>

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

poster

Hallucination remains a key challenge in applying large language models (LLMs) to structured query generation, especially for semi-private or domain-specific languages underrepresented in public training data. In this work, we focus on hallucination detection in these low-resource structured language scenarios, using Splunk Search Processing Language (SPL) as a representative case study. We start from analyzing real-world SPL generation to define hallucination in this context and introduce a comprehensive taxonomy. To enhance detection performance, we propose the Self-Debating framework, which prompts an LLM to generate contrastive explanations from opposing perspectives before rendering a final consistency judgment. We also construct a synthetic benchmark, SynSPL, to support systematic evaluation of hallucination detection in SPL generation. Experimental results show that Self-Debating consistently outperforms LLM-as-a-Judge baselines with zero-shot and chain-of-thought (CoT) prompts in SPL hallucination detection across different LLMs, yielding 5–10% relative gains in hallucination F1 scores on both real and synthetic datasets, and up to 260% improvement for LLaMA-3.1–8B. Besides hallucination detection on SPL, Self-Debating also achieves excellent performance on the FaithBench benchmark for summarization hallucination, demonstrating the strong generalization ability of Self-Debating, with OpenAI o1-mini achieving state-of-the-art performance. All these results consistently demonstrate the strong robustness and wide generalizability of Self-Debating.

Hallucination Detection in Structured Query Generation via LLM Self-Debating

Retrieval-augmented generation (RAG) improves large language models (LMs) by incorporating non-parametric knowledge through evidence retrieved from external sources. However, it often struggles to cope with inconsistent and irrelevant information that can distract the LM from its tasks, especially when multiple evidence pieces are required. While compressing the retrieved evidence with a compression model aims to address this issue, the compressed evidence may still be unfamiliar to the target model used for downstream tasks, potentially failing to utilize the evidence effectively. We propose FaviComp (Familarity-Aware Evidence Compression), a novel training-free evidence compression technique that makes retrieved evidence more familiar to the target model, while seamlessly integrating parametric knowledge from the model. Experimental results show that FaviComp consistently outperforms the most recent evidence compression baselines across multiple open-domain QA datasets, improving accuracy by up to 28.1% while achieving high compression rates. Additionally, we demonstrate the effective integration of both parametric and non-parametric knowledge during evidence compression.

Familiarity-Aware Evidence Compression for Retrieval-Augmented Generation

Traditional voice conversion (VC) methods typically attempt to separate speaker identity and linguistic information into distinct representations, which are then combined to reconstruct the audio. However, effectively disentangling these factors remains challenging, often leading to information loss during training. In this paper, we propose a new approach that leverages synthetic speech data generated by a high-quality, pretrained multispeaker text-to-speech (TTS) model. Specifically, synthetic data pairs that share the same linguistic content but differ in speaker identity are used as input-output pairs to train the voice conversion model. This enables the model to learn a direct mapping between source and target voices, effectively capturing speaker-specific characteristics while preserving linguistic content. Additionally, we introduce a flexible training strategy for any-to-any voice conversion that generalizes well to unseen speakers and new languages, enhancing adaptability and performance in zero-shot scenarios. Our experiments show that our proposed method achieves a 16.35% relative reduction in word error rate and a 5.91% improvement in speaker cosine similarity, outperforming several state-of-the-art methods. Voice conversion samples can be accessed at: https://voiceconversion-emnlp-2025.github.io/

O_O-VC: Synthetic Data-Driven One-to-One Alignment for Any-to-Any Voice Conversion

In a rapidly evolving world where information updates swiftly, knowledge in large language models (LLMs) becomes outdated quickly. Retraining LLMs is not a cost-effective option, making knowledge editing (KE) without modifying parameters particularly necessary. We find that although existing retrieval-augmented generation (RAG)-based KE methods excel at editing simple knowledge, they struggle with KE in multi-hop question answering due to the issue of ''edit skipping'', which refers to skipping the relevant edited fact in inference. In addition to the diversity of natural language expressions of knowledge, edit skipping also arises from the mismatch between the granularity of LLMs in problem-solving and the facts in the edited memory. To address this issue, we propose a novel Iterative Retrieval-Augmented Knowledge Editing method with guided decomposition (IRAKE) through the guidance from single edited facts and entire edited cases. Experimental results demonstrate that IRAKE mitigates the failure of editing caused by edit skipping and outperforms state-of-the-art methods for KE in multi-hop question answering.

Avoiding Knowledge Edit Skipping in Multi-hop Question Answering with Guided Decomposition

Socratic teaching, known for its emphasis on heuristic questioning and deep thinking, has demonstrated significant advantages in promoting students' cognitive development. However, traditional Socratic teaching places high demands on teachers' expertise and real-time feedback capabilities, making it difficult to scale in large educational settings. Recent breakthroughs in large language models (LLMs) in natural language generation and dialogue comprehension offer the potential for automated Socratic teaching. In this paper, we propose Knowledge-Enlightened Learning Enhanced by LLMs (KELE), a novel multi-agent framework for structured Socratic teaching with LLMs. KELE constructs a structured Socratic teaching rule system (SocRule) and a “consultant–teacher” multi-agent collaborative teaching mechanism, in which two LLMs respectively take charge of teaching planning and execution, ensuring a logically coherent and hierarchically structured Socratic teaching process. We also construct SocratDataset, a structured Socratic teaching dataset covering 34 teaching strategies and over 42,000 dialogue turns, and train SocratTeachLLM, a specialized LLM for Socratic teaching tasks. Additionally, we build a comprehensive Socratic teaching quality evaluation system for LLMs, covering 9 dimensions from single-turn dialogue to multi-turn teaching processes. Experimental results show that SocratTeachLLM significantly outperforms GPT-4o, which has a much larger parameter size, across all Socratic teaching capabilities.

KELE: A Multi-Agent Framework for Structured Socratic Teaching with Large Language Models

Large Language Models (LLMs) have made significant strides in problem-solving by incorporating reasoning processes. However, this enhanced reasoning capability results in an increased number of output tokens during inference, leading to higher computational costs. To address this challenge, we propose TwT (Thinking without Tokens), a method that reduces inference-time costs through habitual reasoning distillation with multi-teachers' guidance, while maintaining high performance. Our approach introduces a Habitual Reasoning Distillation method, which internalizes explicit reasoning into the model's habitual behavior through a Teacher-Guided compression strategy inspired by human cognition. Additionally, we propose Dual-Criteria Rejection Sampling (DCRS), a technique that generates a high-quality and diverse distillation dataset using multiple teacher models, making our method suitable for unsupervised scenarios. Experimental results demonstrate that TwT effectively reduces inference costs while preserving superior performance, achieving up to a 13.6% improvement in accuracy with fewer output tokens compared to other distillation methods, offering a highly practical solution for efficient LLM deployment.

TwT: Thinking without Tokens by Habitual Reasoning Distillation with Multi-Teachers’ Guidance

As large language models (LLMs) are increasingly applied to real-world scenarios, it becomes crucial to understand their ability to follow multiple instructions simultaneously. To systematically evaluate these capabilities, we introduce two specialized benchmarks for fundamental domains where multiple instructions following is important: Many Instruction-Following Eval (ManyIFEval) for text generation with up to ten instructions, and Style-aware Mostly Basic Programming Problems (StyleMBPP) for code generation with up to six instructions. Our experiments with the created benchmarks across ten LLMs reveal that performance consistently degrades as the number of instructions increases. Furthermore, given the fact that evaluating all the possible combinations of multiple instructions is computationally impractical in actual use cases, we developed three types of regression models that can estimate performance on both unseen instruction combinations and different numbers of instructions which are not used during training. We demonstrate that a logistic regression model using instruction count as an explanatory variable can predict performance of following multiple instructions with approximately 10% error, even for unseen instruction combinations. Furthermore, we show that relatively modest sample sizes (500 for ManyIFEval and 300 for StyleMBPP) are sufficient for performance estimation, enabling efficient evaluation of LLMs under various instruction combinations.

When Instructions Multiply: Measuring and Estimating LLM Capabilities of Multiple Instructions Following

The emergence of the tool agent paradigm has broadened the capability boundaries of the Large Language Model (LLM), enabling it to complete more complex tasks. However, the effectiveness of this paradigm is limited due to the issue of parameter failure during its execution. To explore this phenomenon and propose corresponding suggestions, we first construct a parameter failure taxonomy in this paper. We derive five failure categories from the invocation chain of a mainstream tool agent. Then, we explore the correlation between three different input sources and failure categories by applying 15 input perturbation methods to the input. Experimental results show that parameter name hallucination failure primarily stems from inherent LLM limitations, while issues with input sources mainly cause other failure patterns. To improve the reliability and effectiveness of tool-agent interactions, we propose corresponding improvement suggestions, including standardizing tool return formats, improving error feedback mechanisms, and ensuring parameter consistency.

Butterfly Effects in Toolchains: A Comprehensive Analysis of Failed Parameter Filling in LLM Tool-Agent Systems

Large Language Models (LLMs) have demonstrated remarkable performance across various tasks. However, in long-form question answering (LFQA), they often struggle with factual accuracy, frequently generating hallucinated responses. In this work, we introduce FinLFQA, a benchmark designed to evaluate LLMs' ability to generate answers with reliable attributions. FinLFQA evaluates three key aspects: (1) evidence-supported content to enhance factual grounding and verifiability, (2) step-by-step calculations using executable code for numerical reliability, and (3) domain-specific reasoning informed by knowledge. We conduct an extensive evaluation of eight LLMs, leveraging the developed automated evaluation protocol to evaluate their performance. Our findings show that GPT-4o outperforms other models, while open-sourced models are closing the gap with proprietary models, which demonstrates that open-source models are becoming competitive alternatives for real-world applications. We also find that post-hoc and end-to-end generation perform similarly, while iterative self-feedback provides no significant improvement except external signal is provided.

FinLFQA: Evaluating Attributed Text Generation of LLMs in Financial Long-Form Question Answering

The recent successful paradigm of solving logical reasoning problems with tool-augmented large language models (LLMs) leverages translation of natural language (NL) statements into First-Order Logic (FOL) and external theorem provers. However, the correctness of FOL statements, comprising operators and text, often go unverified due to the lack of a reliable evaluation metric for comparing generated and ground-truth FOLs. In this paper, we conduct a comprehensive study on the sensitivity of existing metrics---NL, FOL, and graph-based--- and their alignment with LLM as a judge on FOL evaluation to measure robustness. We introduce operator and text-based perturbations to ground-truth FOL statements to assess metric sensitivity. We then evaluate metric robustness by comparing them against LLMs judgement. Our empirical findings highlight a clear oversensitivity in the n-gram metric BLEU for text perturbations. The operator perturbation affects the semantic graph metric Smatch++ for structural changes, and the FOL metric for specific operator changes. We observe a closer alignment between BertScore and LLM judgement, proving the importance of semantic evaluation. Additionally, we show that combining metrics enhances both robustness and sensitivity compared to using individual metrics.

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Next from EMNLP 2025

Hallucination Detection in Structured Query Generation via LLM Self-Debating

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