GenAI4ELab/papercli-papers huggingface dataset:
HierDiffuse: Progressive Diffusion for Robust Interest Fusion in CTR Prediction
Authors: Ziheng Ni; Congcong Liu; Yuying Chen; Zhiwei Fang; Changping Peng; Zhangang Lin; Ching Law; Jingping Shao
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Modern recommendation systems grapple with reconciling users’ enduring preferences with transient interests, particularly in click-through rate (CTR) prediction. Existing approaches inadequately fuse long-term behavioral profiles (e.g., aggregated purchase trends) and short-term interaction sequences (e.g., real-time clicks), suffering from representational misalignment and noise in transient signals. We propose HierDiffuse, a unified framework that redefines interest fusion as a hierarchical denoising process through diffusion models. Our approach addresses these challenges via three innovations: (1) A cross-scale diffusion mechanism aligns long- and short-term representations by iteratively refining long-term interests using short-term contextual guidance; (2) A Semantic Guidance Disentanglement (SGD) mechanism explicitly decouples core interests from noise in short-term signals;(3) Trajectory Convergence Constraint (TCC) is proposed to accelerate diffusion model reasoning without reducing generation quality to meet the constraints of high QPS (Queries Per Second) and low latency for online deployment of recommendation or advertising systems.HierDiffuse eliminates ad-hoc fusion operators, dynamically integrates multi-scale interests, and enhances robustness to spurious interactions as well as improves inference speed. Extensive experiments on real-world datasets demonstrate state-of-the-art performance, with significant improvements in CTR prediction accuracy and robustness to noisy interactions. Our work establishes diffusion models as a principled paradigm for adaptive interest fusion in recommendation systems.
TOBUGraph: Knowledge Graph-Based Retrieval for Enhanced LLM Performance Beyond RAG
Authors: Savini Kashmira; Jayanaka L. Dantanarayana; Joshua Brodsky; Ashish Mahendra; Yiping Kang; Krisztian Flautner; Lingjia Tang; Jason Mars
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Retrieval-Augmented Generation (RAG) is one of the leading and most widely used techniques for enhancing LLM retrieval capabilities, but it still faces significant limitations in commercial use cases. RAG primarily relies on the query-chunk text-to-text similarity in the embedding space for retrieval and can fail to capture deeper semantic relationships across chunks, is highly sensitive to chunking strategies, and is prone to hallucinations. To address these challenges, we propose TOBUGraph, a graph-based retrieval framework that first constructs the knowledge graph from unstructured data dynamically and automatically. Using LLMs, TOBUGraph extracts structured knowledge and diverse relationships among data, going beyond RAG’s text-to-text similarity. Retrieval is achieved through graph traversal, leveraging the extracted relationships and structures to enhance retrieval accuracy. This eliminates the need for chunking configurations while reducing hallucination. We demonstrate TOBUGraph’s effectiveness in TOBU, a real-world application in production for personal memory organization and retrieval. Our evaluation using real user data demonstrates that TOBUGraph outperforms multiple RAG implementations in both precision and recall, significantly enhancing user experience through improved retrieval accuracy.
Thinking with DistilQwen: A Tale of Four Distilled Reasoning and Reward Model Series
Authors: Wenrui Cai; Chengyu Wang; Junbing Yan; Jun Huang; Xiangzhong Fang
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Recently, the demand for small and efficient reasoning models to support real-world applications has driven the development of knowledge distillation techniques that balance reasoning performance and inference speed. In this paper, we further extend the DistilQwen model family, initialized from the Qwen models, by introducing four model series specifically designed to meet industrial requirements. The distilled model collection comprises: (1) slow-thinking models, optimized for reasoning tasks that require high accuracy; (2) two series of adaptive-thinking models, which dynamically adjust reasoning strategies based on input tasks to maximize efficiency across diverse scenarios; and (3) distilled reward models, which enable further reinforcement learning of reasoning models using distilled knowledge. Comprehensive evaluations across multiple benchmarks demonstrate both high inference efficiency and strong reasoning performance for these models, as well as the practical utility of distilled reward models. We further show that these models support industry practitioners by providing scalable training and inference functionalities on the Alibaba Cloud PAI (Platform for Artificial Intelligence) platform.
Crossing Domains without Labels: Distant Supervision for Term Extraction
Authors: Elena Senger; Yuri Campbell; Rob Van Der Goot; Barbara Plank
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Automatic Term Extraction (ATE) is a critical component in downstream NLP tasks such as document tagging, ontology construction and patent analysis. Current state-of-the-art methods require expensive human annotation and struggle with domain transfer, limiting their practical deployment. This highlights the need for more robust, scalable solutions and realistic evaluation settings. To address this, we introduce a comprehensive benchmark spanning seven diverse domains, enabling performance evaluation at both the document- and corpus-levels. Furthermore, we propose a robust LLM-based model that outperforms both supervised cross-domain encoder models and few-shot learning baselines and performs competitively with its GPT-4o teacher on this benchmark.The first step of our approach is generating psuedo-labels with this black-box LLM on general and scientific domains to ensure generalizability. Building on this data, we fine-tune the first LLMs for ATE. To further enhance document-level consistency, oftentimes needed for downstream tasks, we introduce lightweight post-hoc heuristics. Our approach exceeds previous approaches on 5/7 domains with an average improvement of 10 percentage points. We release our dataset and fine-tuned models to support future research in this area.
I-SEE: An Instruction-tuned, SOP-Enhanced Quality Evaluator for Product Content
Authors: Aniket Joshi; Cyrus Andre DSouza; Sejal Jain; Jitenkumar Babubhai Rana; Promod Yenigalla
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: High-quality content is critical for driving customer satisfaction and conversions across digital platforms and e-commerce. Ensuring that essential information is complete, accurate, and aligned with customer expectations presents a significant challenge at scale. Existing approaches to content evaluation often treat all information uniformly, without prioritizing based on customer relevance, and rely heavily on manual prompt design to encode domain expertise into Large Language Models (LLMs). We present ISEE, a unified framework that addresses these limitations through three core innovations: (1) automated identification of customer-impacting features by synthesizing signals from search behavior, queries, and feedback, enabling targeted content improvements; (2) an instruction-tuned multimodal LLM trained to reliably follow structured operational guidelines, reducing dependence on manual prompt engineering; and (3) robust zero-shot generalization to new product content, features and SOPs via targeted instruction tuning. Evaluated across 20 product categories and 150 product specific features, ISEE achieves 90% precision at 78% recall in detecting content inconsistencies, outperforming much larger (> 200B parameters) models while using a compact 12B architecture.
Computational Blueprints: Generating Isomorphic Mathematics Problems with Large Language Models
Authors: Jeong-Hoon Kim; Jinwoo Nam; Geunsik Jo
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Personalized mathematics education is growing rapidly, creating a strong demand for large sets of similar practice problems.Yet existing studies on mathematics problem generation have focused on data augmentation for training neural language models rather than on direct educational deployment. To bridge this gap, we define a new task, Isomorphic Math Problem Generation (IMPG), designed to produce structurally consistent variants of source problems. Subsequently, we explored LLM-based frameworks for automatic IMPG through successive refinements, and established Computational Blueprints for Isomorphic Twins (CBIT).With meta-level generation and template-based selective variation, CBIT achieves high mathematical correctness and structural consistency while reducing the cost of generation.Empirical results across refinements demonstrate that CBIT is superior on generation accuracy and cost-effectiveness at scale.Most importantly, CBIT-generated problems exhibited an error rate 17.8% lower than expert-authored items, with deployment to 6,732 learners on a commercial education platform yielding 186,870 interactions.
Fin-ExBERT: User Intent based Text Extraction in Financial Context using Graph-Augmented BERT and trainable Plugin
Authors: Soumick Sarker; Abhijit Kumar Rai
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Financial dialogue transcripts pose a unique challenge for sentence-level information extraction due to their informal structure, domain-specific vocabulary, and variable intent density. We introduce Fin-ExBERT, a lightweight and modular framework for extracting user intent–relevant sentences from annotated financial service calls. Our approach builds on a domain-adapted BERT (Bidirectional Encoder Representations from Transformers) backbone enhanced with LoRA (Low-Rank Adaptation) adapters, enabling efficient fine-tuning using limited labeled data. We propose a two-stage training strategy with progressive unfreezing: initially training a classifier head while freezing the backbone, followed by gradual fine-tuning of the entire model with differential learning rates. To ensure robust extraction under uncertainty, we adopt a dynamic thresholding strategy based on probability curvature (elbow detection), avoiding fixed cutoff heuristics. Empirical results show strong precision and F1 performance on real-world transcripts, with interpretable output suitable for downstream auditing and question-answering workflows. The full framework supports batched evaluation, visualization, and calibrated export, offering a deployable solution for financial dialogue mining.
DecEx-RAG: Boosting Agentic Retrieval-Augmented Generation with Decision and Execution Optimization via Process Supervision
Authors: Yongqi Leng; Yikun Lei; Xikai Liu; Meizhi Zhong; Bojian Xiong; Yurong Zhang; Yan Gao; Yiwu; Yao Hu; Deyi Xiong
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Agentic Retrieval-Augmented Generation (Agentic RAG) enhances the processing capability for complex tasks through dynamic retrieval and adaptive workflows. Recent advances (e.g., Search-R1) have shown that outcome-supervised reinforcement learning demonstrate strong performance. However, this approach still suffers from inefficient exploration, sparse reward signals, and ambiguous global reward feedback.To address these challenges, we propose DecEx-RAG, which models RAG as a Markov Decision Process (MDP) incorporating decision-making and execution, while introducing an efficient pruning strategy to optimize data expansion. Through comprehensive process-level policy optimization, DecEx-RAG significantly enhances the autonomous task decomposition, dynamic retrieval, and high-quality answer generation capabilities of large language models (LLMs). Experiments show that DecEx-RAG achieves an average absolute performance improvement of 6.2% across six datasets, significantly outperforming existing baselines. Moreover, the pruning strategy improves data construction efficiency by nearly 6 ×, providing an efficient solution for process-supervised RAG training. The code is available at https://github.com/sdsxdxl/DecEx-RAG.
FLOW-BENCH: Towards Conversational Generation of Enterprise Workflows
Authors: Evelyn Duesterwald; Siyu Huo; Vatche Isahagian; K. R. Jayaram; Ritesh Kumar; Vinod Muthusamy; Punleuk Oum; Debashish Saha; Gegi Thomas; Praveen Venkateswaran
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Large Language Models (LLMs) can be used to convert natural language (NL) instructions into structured business process automation (BPA) process artifacts.This paper contributes (i) FLOW-BENCH, a high quality dataset of paired NL instructions and business process definitions toevaluate NL-based BPA tools, and support research in this area, and (ii) FLOW-GEN,our approach to utilize LLMs to translate NL into an intermediate Python representation that facilitates final conversion into widely adopted business process definition languages, such as BPMN and DMN. We bootstrap FLOW-BENCH by demonstrating how it can be used to evaluate the components of FLOW-GEN across eight LLMs. We hope that FLOW-GEN and FLOW-BENCHcatalyze further research in BPA.
Format Inertia: A Failure Mechanism of LLMs in Medical Pre-Consultation
Authors: Seungseop Lim; Gibaeg Kim; Wooseok Han; Jean Seo; Hyunkyung Lee; Jaehyo Yoo; Eunho Yang
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Recent advances in Large Language Models (LLMs) have brought significant improvements to various service domains, including chatbots and medical pre-consultation applications. In the healthcare domain, the most common approach for adapting LLMs to multi-turn dialogue generation is Supervised Fine-Tuning (SFT). However, datasets for SFT in tasks like medical pre-consultation typically exhibit a skewed turn-count distribution. Training on such data induces a novel failure mechanism we term **Format Inertia**, where models tend to generate repetitive, format-correct, but diagnostically uninformative questions in long medical dialogues. To mitigate this observed failure mechanism, we adopt a simple, data-centric method that rebalances the turn-count distribution of the training dataset. Experimental results show that our approach substantially alleviates Format Inertia in medical pre-consultation.
Extraction of Information Provision Activity Requirements from EU Acquis
Authors: Jakub Piskorski; Dominik Skotarczak
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: 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. This task comprises the identification of text fragments that introduce an obligation to provide information, and the extraction of structured information about the key entities involved along with the temporal modalities. We compare various technologies for this task, i.e. knowledge-, classical ML-, transformer-, and generative AI-based approaches, on a new benchmark corpus.
Contrastive Learning Using Graph Embeddings for Domain Adaptation of Language Models in the Process Industry
Authors: Anastasia Zhukova; Jonas Lührs; Christian E. Lobmüller; Bela Gipp
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Recent trends in NLP utilize knowledge graphs (KGs) to enhance pretrained language models by incorporating additional knowledge from the graph structures to learn domain-specific terminology or relationships between documents that might otherwise be overlooked. This paper explores how SciNCL, a graph-aware neighborhood contrastive learning methodology originally designed for scientific publications, can be applied to the process industry domain, where text logs contain crucial information about daily operations and are often structured as sparse KGs. Our experiments demonstrate that language models fine-tuned with triplets derived from graph embeddings (GE) outperform a state-of-the-art mE5-large text encoder by 9.8-14.3% (5.45-7.96p) on the proprietary process industry text embedding benchmark (PITEB) while having 3 times fewer parameters.
From Feedback to Checklists: Grounded Evaluation of AI-Generated Clinical Notes
Authors: Karen Zhou; John Michael Giorgi; Pranav Mani; Peng Xu; Davis Liang; Chenhao Tan
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: AI-generated clinical notes are increasingly used in healthcare, but evaluating their quality remains a challenge due to high subjectivity and limited scalability of expert review. Existing automated metrics often fail to align with real-world physician preferences. To address this, we propose a pipeline that systematically distills real user feedback into structured checklists for note evaluation. These checklists are designed to be interpretable, grounded in human feedback, and enforceable by LLM-based evaluators. Using deidentified data from over 21,000 clinical encounters (prepared in accordance with the HIPAA safe harbor standard) from a deployed AI medical scribe system, we show that our feedback-derived checklist outperforms a baseline approach in our offline evaluations in coverage, diversity, and predictive power for human ratings. Extensive experiments confirm the checklist’s robustness to quality-degrading perturbations, significant alignment with clinician preferences, and practical value as an evaluation methodology. In offline research settings, our checklist offers a practical tool for flagging notes that may fall short of our defined quality standards.
FlexDoc: Parameterized Sampling for Diverse Multilingual Synthetic Documents for Training Document Understanding Models
Authors: Karan Dua; Hitesh Laxmichand Patel; Puneet Mittal; Ranjeet Gupta; Amit Agarwal; Praneet Pabolu; Srikant Panda; Hansa Meghwani; Graham Horwood; Fahad Shah
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Developing document understanding models at enterprise scale requires large, diverse, and well-annotated datasets spanning a wide range of document types. However, collecting such data is prohibitively expensive due to privacy constraints, legal restrictions, and the sheer volume of manual annotation needed - costs that can scale into millions of dollars. We introduce FlexDoc, a scalable synthetic data generation framework that combines Stochastic Schemas and Parameterized Sampling to produce realistic, multilingual semi-structured documents with rich annotations. By probabilistically modeling layout patterns, visual structure, and content variability, FlexDoc enables the controlled generation of diverse document variants at scale. Experiments on Key Information Extraction (KIE) tasks demonstrate that FlexDoc-generated data improves the absolute F1 Score by up to 11% when used to augment real datasets, while reducing annotation effort by over 90% compared to traditional hard-template methods. The solution is in active deployment, where it has accelerated the development of enterprise-grade document understanding models while significantly reducing data acquisition and annotation costs.
GEMMAS: Graph-based Evaluation Metrics for Multi Agent Systems
Authors: Jisoo Lee; Raeyoung Chang; Dongwook Kwon; Harmanpreet Singh; Nikhil Verma
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Multi-agent systems built on language models have shown strong performance on collaborative reasoning tasks. However, existing evaluations focus only on the correctness of the final output, overlooking how inefficient communication and poor coordination contribute to redundant reasoning and higher computational costs. We introduce **GEMMAS**, a graph-based evaluation framework that analyzes the internal collaboration process by modeling agent interactions as a directed acyclic graph. To capture collaboration quality, we propose two process-level metrics: Information Diversity Score (IDS) to measure semantic variation in inter-agent messages, and Unnecessary Path Ratio (UPR) to quantify redundant reasoning paths. We evaluate GEMMAS across five benchmarks and highlight results on GSM8K, where systems with only a 2.1% difference in accuracy differ by 12.8% in IDS and 80% in UPR, revealing substantial variation in internal collaboration. These findings demonstrate that outcome-only metrics are insufficient for evaluating multi-agent performance and highlight the importance of process-level diagnostics in designing more interpretable and resource-efficient collaborative AI systems.
Knowledge-Augmented Question Error Correction for Chinese Question Answer System with QuestionRAG
Authors: Longpeng Qiu; Ting Li; Shuai Mao; Nan Yang; Xiaohui Yan
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Input errors in question-answering (QA) systems often lead to incorrect responses. Large language models (LLMs) struggle with this task, frequently failing to interpret user intent (misinterpretation) or unnecessarily altering the original question’s structure (over-correction).We propose QuestionRAG, a framework that tackles these problems. To address misinterpretation, it enriches the input with external knowledge (e.g., search results, related entities). To prevent over-correction, it uses reinforcement learning (RL) to align the model’s objective with precise correction, not just paraphrasing.Our results demonstrate that knowledge augmentation is critical for understanding faulty questions. Furthermore, RL-based alignment proves significantly more effective than traditional supervised fine-tuning (SFT), boosting the model’s ability to follow instructions and generalize. By integrating these two strategies, QuestionRAG unlocks the full potential of LLMs for the question correction task.