China

We address the problem of data scarcity in harmful text classification for guardrailing applications and introduce GRAID (Geometric and Reflective AI-Driven Data Augmentation), a novel pipeline that leverages Large Language Models (LLMs) for dataset augmentation. GRAID consists of two stages: (i) generation of geometrically controlled examples using a constrained LLM, and (ii) augmentation through a multi-agentic reflective process that promotes stylistic diversity and uncovers edge cases. This combination enables both reliable coverage of the input space and nuanced exploration of harmful content. Using two benchmark data sets, we demonstrate that augmenting a harmful text classification dataset with GRAID leads to significant improvements in downstream guardrail model performance.

EMNLP 2025

GRAID: Synthetic Data Generation with Geometric Constraints and Multi-Agentic Reflection for Harmful Content Detection

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.

Systematic reviews in medicine play a critical role in evidence-based decision-making by aggregating findings from multiple studies. A central bottleneck in automating this process is extracting numeric evidence and determining study-level conclusions for specific outcomes and comparisons. Prior work has framed this problem as a textual inference task by retrieving relevant content fragments and inferring conclusions from them. However, such approaches often rely on shallow textual cues and fail to capture the underlying numeric reasoning behind expert assessments. In this work, we conceptualise the problem as one of quantitative reasoning. Rather than inferring conclusions from surface text, we extract structured numerical evidence (e.g., event counts or standard deviations) and apply domain knowledge informed logic to derive outcome-specific conclusions. We develop a numeric reasoning system composed of a numeric data extraction model and an effect estimate component, enabling more accurate and interpretable inference aligned with the domain expert principles. We train the numeric data extraction model using different strategies, including supervised fine-tuning (SFT) and reinforcement learning (RL) with a new value reward model. When evaluated on the CochraneForest benchmark, our best-performing approach -- using RL to train a small-scale number extraction model -- yields up to a 21% absolute improvement in F1 score over retrieval-based systems and outperforms general-purpose LLMs of over 400B parameters by up to 9%. Our results demonstrate the promise of reasoning-driven approaches for automating systematic evidence synthesis.

Enhancing Study-Level Inference from Clinical Trial Papers via Reinforcement Learning-Based Numeric Reasoning

Innovators often exhibit cognitive fixation on existing solutions or nascent ideas, hindering the exploration of novel alternatives. This paper introduces a methodology for constructing Functional Concept Graphs (FCGs), interconnected representations of functional elements that support abstraction, problem reframing, and analogical inspiration. Our approach yields large-scale, high-quality FCGs with explicit abstraction relations, overcoming limitations of prior work. We further present MUSE, an algorithm leveraging FCGs to generate creative inspirations for a given problem. We demonstrate our method by computing an FCG on 500K patents, which we release for further research. We introduced MUSE, a novel engine to find unexpected solutions to problems. This engine consists of the inspiration graph, whose problem and solution nodes were extracted from 500K patent descriptions. For a given problem, MUSE aims to enhance users' creative problem solving by providing them with inspirations sampled from the inspiration graph. A user study indicates that participants exposed to MUSE's inspirations generated more creative ideas, both in terms of absolute number (up to 19% increase over participants not given inspirations) and ratio (75%, compared to 49% for no inspirations).

Finding your MUSE: Mining Unexpected Solutions Engine

Recent advancements in large language models have significantly influenced the field of online medical consultations. However, critical challenges remain, such as the generation of hallucinated information and the integration of up-to-date medical knowledge. To address these issues, we propose **I**nformatics **Llama** (ILlama), a novel framework that combines retrieval-augmented generation with a structured medical knowledge graph. ILlama incorporates relevant medical knowledge by transforming subgraphs from a structured medical knowledge graph into text for retrieval-augmented generation. By generating subgraphs from the medical knowledge graph in advance, specifically focusing on diseases and symptoms, ILlama is able to enhance the accuracy and relevance of its medical reasoning. This framework enables effective incorporation of causal relationships between symptoms and diseases. Also, it delivers context-aware consultations aligned with user queries. Experimental results on the two medical consultation datasets demonstrate that ILlama outperforms the strong baselines, achieving a semantic similarity F1-score of 0.884 when compared with ground truth consultation answers. Furthermore, qualitative analysis of ILlama’s responses reveals significant improvements in hallucination reduction and clinical usefulness. These results suggest that ILlama has strong potential as a reliable tool for real-world medical consultation environments.

Leveraging Knowledge Graph-Enhanced LLMs for Context-Aware Medical Consultation

Natural language generation (NLG) metrics play a central role in evaluating generated texts, but are not well suited for the structural and legal characteristics of patent documents. Large language models (LLMs) offer strong potential in automating patent generation, yet research on evaluating LLM-generated patents remains limited, especially in evaluating the generation quality of patent claims, which are central to defining the scope of protection. Effective claim evaluation requires addressing legal validity, technical accuracy, and structural compliance. To address this gap, we introduce PatentScore, a multi-dimensional evaluation framework for assessing LLM-generated patent claims. PatentScore incorporates: (1) hierarchical decomposition for claim analysis; (2) domain-specific validation patterns based on legal and technical standards; and (3) scoring across structural, semantic, and legal dimensions. Unlike general-purpose NLG metrics, PatentScore reflects patent-specific constraints and document structures, enabling evaluation beyond surface similarity. We evaluate 400 GPT-4o-mini generated Claim 1s and report a Pearson correlation of r = 0.819 with expert annotations, outperforming existing NLG metrics. Furthermore, we conduct additional evaluations using open models such as Claude-3.5-Haiku and Gemini-1.5-flash, all of which show strong correlations with expert judgments, confirming the robustness and generalizability of our framework.

PatentScore: Multi-dimensional Evaluation of LLM-Generated Patent Claims

An important task in machine learning (ML) research is comparing prior work, which is often performed via ML leaderboards: a tabular overview of experiments with comparable conditions (e.g. same task, dataset, and metric). However, the growing volume of literature creates challenges in creating and maintaining these leaderboards. To ease this burden, researchers have developed methods to extract leaderboard entries from research papers for automated leaderboard curation. Yet, prior work varies in problem framing, complicating comparisons and limiting real-world applicability. In this position paper, we present the first overview of Automatic Leaderboard Generation (ALG) research, identifying fundamental differences in assumptions, scope, and output formats. We propose an ALG unified conceptual framework to standardise how the ALG task is defined. We offer ALG benchmarking guidelines, including recommendations for datasets and metrics that promote fair, reproducible evaluation. Lastly, we outline challenges and new directions for ALG, advocating for broader coverage by including all reported results and richer metadata.

A Position Paper on the Automatic Generation of Machine Learning Leaderboards

Visual Document Retrieval (VDR) typically operates as text-to-image retrieval using specialized bi-encoders trained to directly embed document images. We revisit a zero-shot generate-and-encode pipeline: a vision–language model first produces a detailed textual description of each document image, which is then embedded by a standard text encoder. On the ViDoRe-v2 benchmark, the method reaches 63.4% nDCG@5, surpassing the strongest specialised multi-vector visual document encoder. Analysis shows that modern vision–language models capture complex textual and visual cues with sufficient granularity to act as a reusable semantic proxy. By off-loading modality alignment to pretrained vision–language models, our approach removes the need for computationally intensive text-image contrastive training and establishes a strong zero-shot baseline for future VDR systems.

SERVAL: Surprisingly Effective Zero-Shot Visual Document Retrieval Powered by Large Vision and Language Models

Multimodal large language models (MLLMs) have shown impressive capabilities in document understanding, a rapidly growing research area with significant industrial demand. As a multimodal task, document understanding requires models to possess both perceptual and cognitive abilities. However, due to different types of annotation noise in training, current MLLMs often face conflicts between perception and cognition. Taking a document VQA task (cognition) as an example, an MLLM might generate answers that do not match the corresponding visual content identified by its OCR (perception). This conflict suggests that the MLLM might struggle to establish an intrinsic connection between the information it “sees” and what it “understands”. Such conflicts challenge the intuitive notion that cognition is consistent with perception, hindering the performance and explainability of MLLMs. In this paper, we define the conflicts between cognition and perception as Cognition and Perception (C&P) knowledge conflicts, a form of multimodal knowledge conflicts, and systematically assess them with a focus on document understanding. Our analysis reveals that even GPT-4o, a leading MLLM, achieves only 75.26% C&P consistency. To mitigate the C&P knowledge conflicts, we propose a novel method called Multimodal Knowledge Consistency Fine-tuning. Our method reduces C&P knowledge conflicts across all tested MLLMs and enhances their performance in both cognitive and perceptual tasks. All data we construct will be publicly available.

Is Cognition Consistent with Perception? Assessing and Mitigating Multimodal Knowledge Conflicts in Document Understanding

Multimodal document retrieval aims to identify and retrieve various forms of multimodal content, such as figures, tables, charts, and layout information from extensive documents. Despite its increasing popularity, there is a notable lack of a comprehensive and robust benchmark to effectively evaluate the performance of systems in such tasks. To address this gap, this work introduces a new benchmark, named MMDocIR, that encompasses two distinct tasks: page-level and layout-level retrieval. The former evaluates the performance of identifying the most relevant pages within a long document, while the later assesses the ability of detecting specific layouts, providing a more fine-grained measure than whole-page analysis. A layout refers to a variety of elements, including textual paragraphs, equations, figures, tables, or charts. The MMDocIR benchmark comprises a rich dataset featuring 1,685 questions annotated by experts and 173,843 questions with bootstrapped labels, making it a valuable resource in multimodal document retrieval for both training and evaluation. Through rigorous experiments, we demonstrate that (i) visual retrievers significantly outperform their text counterparts, (ii) MMDocIR training set effectively enhances the performance of multimodal document retrieval and (iii) text retrievers leveraging VLM-text significantly outperforms retrievers relying on OCR-text.

MMDocIR: Benchmarking Multimodal Retrieval for Long Documents

Despite continuous advancements in the capabilities of large language models (LLMs), numerical reasoning remains a challenging area. Techniques like chain-of-thought prompting, tree-of-thought prompting, and program-of-thought prompting guide LLMs through intermediate reasoning steps. Although in-context learning with few-shot prompting has improved performance, LLMs still lag behind state-of-the-art models on financial numerical reasoning datasets such as FinQA and ConvFinQA. In this work, we introduce FINDER, a novel two-step framework, to enhance LLM's capabilities in financial numerical reasoning. The first step utilizes a generative retriever to extract relevant facts from unstructured data, including both text and tables. This is followed by context-aware Program of Thought prompting with dynamic selection of in-context examples. Our model FINDER achieves a new state-of-the-art performance on both the FinQA and ConvFinQA datasets, surpassing previous benchmarks with execution accuracy improvements of 5.98% and 4.05%, respectively.

Program of Thoughts for Financial Reasoning: Leveraging Dynamic In-Context Examples and Generative Retrieval

Dense Passage Retrieval (DPR) typically relies on Euclidean or Cosine distance to measure query-passage relevance in embedding space. While effective when embeddings lie on a linear manifold, our experiments across DPR benchmarks suggest that embeddings often lie on lower-dimensional, non-linear manifolds, especially in out-of-distribution (OOD) settings, where these distances fail to capture semantic similarity. To address this limitation, we propose a manifold-aware distance metric for DPR (MA-DPR) that models the intrinsic manifold structure of passages using a nearest neighbor graph and measures distance between query and passages based on their shortest path in this graph. We show that MA-DPR outperforms Euclidean and Cosine distance by up to 26% on OOD passage retrieval while maintaining performance on in-distribution data across various embedding models, with only a small increase in query inference time. Empirical evidence suggests that manifold-aware distance allows DPR to leverage context from related neighboring passages, making it effective even in the absence of direct semantic overlap. In addition, it can be extended to a wide range of dense embedding and DPR tasks, offering practical utility across diverse retrieval scenarios.

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Enhancing Study-Level Inference from Clinical Trial Papers via Reinforcement Learning-Based Numeric Reasoning

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