GenAI4ELab/papercli-papers huggingface dataset:
Don’t Forget the Base Retriever! A Low-Resource Graph-based Retriever for Multi-hop Question Answering
Authors: André Melo; Enting Chen; Pavlos Vougiouklis; Chenxin Diao; Shriram Piramanayagam; Ruofei Lai; Jeff Z. Pan
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Traditional Retrieval-augmented Generation systems struggle with complex multi-hop questions, which often require reasoning over multiple passages. While GraphRAG approaches address these challenges, most of them rely on expensive LLM calls. In this paper, we propose GRiever, a lightweight, low-resource, multi-step graph-based retriever for multi-hop QA. Unlike prior work, GRiever does not rely on LLMs and can perform multi-step retrieval in a few hundred milliseconds. It efficiently indexes passages alongside an associated knowledge graph and employs a hybrid retriever combined with aggressive filtering to reduce retrieval latency. Experiments on multi-hop QA datasets demonstrate that GRiever outperforms conventional retrievers and shows strong potential as a base retriever within multi-step agentic frameworks.
Beyond Dynamic Quantization: An Efficient Static Hierarchical Mix-precision Framework for Near-Lossless LLM Compression
Authors: Yi Zhang; Kai Zhang; Zheyang Li; Wenming Tan; Ye Ren; Jilin Hu
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Large language models (LLMs) have achieved overwhelming success but require massive storage and computational resources to support the generative inference. Post-training quantization (PTQ) is a promising approach to reduce memory usage, latency and energy consumption of the deployment of LLMs. However, the presence of outliers makes most existing PTQ methods dedicated to dynamic quantization, which turns out hardware-unfriendly and often leads to large quantization errors in static scenarios. To address the above limitations, we introduce a Static Hierarchical Mix-precision Quantization method (SHMQ), which enables near-lossless and hardware-friendly compression of LLMs. Theoretically, our proposed SHMQ quantifies both inter-layer and intra-layer sensitivity through unified derivations involving Hessian. Specifically, SHMQ conducts a systematic precision allocation strategy, which seamlessly integrates coarse-grained inter-layer and fine-grained intra-layer static mix-precision quantization. Furthermore, the permutation procedure, which reorders sensitive channels and insensitive channels that share similar distribution, is leveraged to mitigate static quantization error. Our proposed SHMQ achieves 75.58% on zero-shot reasoning tasks in W4.8A8 Qwen2.5-7B-Instruct, narrowing the accuracy gap to merely 0.13% while yielding averaged 2.86× practical speedup.
STACKFEED: Structured Textual Actor-Critic Knowledge base editing with FEEDback
Authors: Shashank Kirtania; Naman Gupta; Priyanshu Gupta; Sumit Gulwani; Arun Iyer; Suresh Parthasarathy Iyengar; Arjun Radhakrishna; Sriram K. Rajamani; Gustavo Soares
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: 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.
JaCorpTrack: Corporate History Event Extraction for Tracking Organizational Changes
Authors: Yuya Sawada; Hiroki Ouchi; Yuichiro Yasui; Hiroki Teranishi; Yuji Matsumoto; Taro Watanabe; Masayuki Ishii
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Corporate history in corporate annual reports includes events related to organizational changes, which can provide useful cues for a comprehensive understanding of corporate actions.However, extracting organizational changes requires identifying differences in companies before and after an event, raising concerns about whether existing information extraction systems can accurately capture the relations.This work introduces JaCorpTrack, a novel event extraction task designed to identify events related to organizational changes.JaCorpTrack defines five event types related to organizational changes and is designed to identify the company names before and after each event, as well as the corresponding date.Experimental results indicate that large language models (LLMs) exhibit notable disparities in performance across event types.Our analysis reveals that these systems face challenges in identifying company names before and after events, and in interpreting event types expressed under ambiguous terminology.We will publicly release our dataset and experimental code at https://github.com/naist-nlp/JaCorpTrack
CTR-Guided Generative Query Suggestion in Conversational Search
Authors: Erxue Min; Hsiu-Yuan Huang; Xihong Yang; Min Yang; Xin Jia; Yunfang Wu; Hengyi Cai; Junfeng Wang; Shuaiqiang Wang; Dawei Yin
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Generating effective query suggestions in conversational search requires aligning model outputs with user click preferences. However, directly optimizing for these preferences is difficult because click signals are sparse and inherently noisy. To address this, we propose Generative Query Suggestion (GQS), a generative framework that leverages click modeling to denoise implicit feedback and enables reliable preference optimization for improving real-world user engagement.GQS consists of three key components: (1) a Multi-Source CTR Modeling module that captures diverse contextual signals to estimate fine-grained click-through rates, thereby constructing more reliable user click-preference pairs; (2) a Diversity-Aware Preference Alignment strategy using CTR-weighted Direct Preference Optimization (DPO), which balances relevance and semantic diversity; and (3) a CTR-Calibrated Iterative Optimization process that jointly refines both the CTR model and the query suggestion model across training rounds, enabling effective data reuse.Experiments on two real-world tasks demonstrate that GQS outperforms strong baselines in CTR, relevance, and diversity.
LATTE: Learning Aligned Transactions and Textual Embeddings for Bank Clients
Authors: Egor Fadeev; Dzhambulat Mollaev; Aleksei Shestov; Dima Korolev; Omar Zoloev; Ivan A Kireev; Andrey Savchenko; Maksim Makarenko
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Learning clients embeddings from sequences of their historic communications is central to financial applications. While large language models (LLMs) offer general world knowledge, their direct use on long event sequences is computationally expensive and impractical in real-world pipelines. In this paper, we propose , a contrastive learning framework that aligns raw event embeddings with description-based semantic embeddings from frozen LLMs. Behavioral features based on statistical user descriptions are summarized into short prompts, embedded by the LLM, and used as supervision via contrastive loss. The proposed approach significantly reduces inference cost and input size compared to the conventional processing of complete sequences by LLM. We experimentally show that our method outperforms state-of-the-art techniques for learning event sequence representations on real-world financial datasets while remaining deployable in latency-sensitive environments.
RedOne: Revealing Domain-specific LLM Post-Training in Social Networking Services
Authors: Fei Zhao; Chonggang Lu; Wangyue; Zheyong Xie; Ziyan Liu; Haofu Qian; Jianzhao Huang; Fangcheng Shi; Zijie Meng; Hongcheng Guo; Mingqian He; Xinze Lyu; Zheyu Ye; Weiting Liu; Boyang Wang; Shaosheng Cao
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: As a primary medium for modern information dissemination, social networking services (SNS) have experienced rapid growth, which has proposed significant challenges for platform content management and interaction quality improvement. Recently, the development of large language models (LLMs) has offered potential solutions but existing studies focus on isolated tasks, which not only encounter diminishing benefit from the data scaling within individual scenarios but also fail to flexibly adapt to diverse real-world context. To address these challenges, we introduce RedOne, a domain-specific LLM designed to break the performance bottleneck of single-task baselines and establish a comprehensive foundation for the SNS. RedOne was developed through a three-stage training strategy consisting of continue pretraining, supervised fine-tuning, and preference optimization, using a large-scale real-world dataset. Through extensive experiments, RedOne maintains strong general capabilities, and achieves an average improvement up to 14.02% across 8 major SNS tasks and 7.56% in SNS bilingual evaluation benchmark, compared with base models. Furthermore, through online testing, RedOne reduced the exposure rate in harmful content detection by 11.23% and improved the click page rate in post-view search by 14.95% compared with single-tasks baseline models. These results establish RedOne as a robust domain-specific LLM for SNS, demonstrating excellent generalization across various tasks and promising applicability in real-world scenarios.
High-Quality Medical Dialogue Synthesis for Improving EMR Generation
Authors: Chengze Ge; Yu Xu; Qi Shao; Shengping Liu
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: High-quality doctor–patient dialogues, by which we mean realistic and human-like interactions that are intent-consistent, clinically faithful, and free of contradictions, 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.
Z1: Efficient Test-time Scaling with Code
Authors: Zhaojian Yu; Yinghao Wu; Yilun Zhao; Arman Cohan; Xiao-Ping Zhang
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: 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.
Quality Assessment of Tabular Data using Large Language Models and Code Generation
Authors: Ashlesha Akella; Akshar Kaul; Krishnasuri Narayanam; Sameep Mehta
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Reliable data quality is crucial for downstream analysis of tabular datasets, yet rule-based validation often struggles with inefficiency, human intervention, and high computational costs. We present a three-stage framework that combines statistical inliner detection with LLM-driven rule and code generation. After filtering data samples through traditional clustering, we iteratively prompt LLMs to produce semantically valid quality rules and synthesize their executable validators through code-generating LLMs. To generate reliable quality rules, we aid LLMs with retrieval-augmented generation (RAG) by leveraging external knowledge sources and domain-specific few-shot examples. Robust guardrails ensure the accuracy and consistency of both rules and code snippets. Extensive evaluations on benchmark datasets confirm the effectiveness of our approach.
PARSE: LLM Driven Schema Optimization for Reliable Entity Extraction
Authors: Anubhav Shrimal; Aryan Jain; Soumyajit Chowdhury; Promod Yenigalla
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Structured information extraction from unstructured text is critical for emerging Software 3.0 systems where LLM agents autonomously interact with APIs and tools. Recent approaches apply large language models directly to extraction tasks using existing JSON schemas, often with constraint decoding or reinforcement learning approaches to ensure syntactic validity, but treat JSON schemas as static contracts designed for human developers, leading to suboptimal extraction performance, frequent hallucinations, and unreliable agent behavior when schemas contain ambiguous or incomplete specifications. We recognize that JSON schemas themselves are a form of natural language understanding contract that encodes rules, relationships, and expectations about data structure contracts that LLMs should be able to both interpret and systematically improve. Consequently, we develop PARSE (Parameter Automated Refinement and Schema Extraction), a novel system with two synergistic components: ARCHITECT, which autonomously optimizes JSON schemas for LLM consumption while maintaining backward compatibility through RELAY (an integrated code generation system), and SCOPE, which implements reflection-based extraction with combined static and LLM-based guardrails. We evaluate PARSE qualitatively and quantitatively on three datasets including Schema-Guided Dialogue (SGD), Structured Web Data Extraction (SWDE), and internal retail conversation data, and find that it achieves up to 64.7% improvement in extraction accuracy on SWDE with combined framework improvements reaching 10% across models, while reducing extraction errors by 92% within the first retry and and maintaining practical latency.
From Long Videos to Engaging Clips: A Human-Inspired Video Editing Framework with Multimodal Narrative Understanding
Authors: Xiangfeng Wang; Xiao Li; Yadong Wei; Songxueyu; Yang Song; Xiaxiaoqiang; Fangrui Zeng; Zaiyi Chen; Liuliu; Gu Xu; Tong Xu
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: The rapid growth of online video content, especially on short video platforms, has created a growing demand for efficient video editing techniques that can condense long-form videos into concise and engaging clips. Existing automatic editing methods predominantly rely on textual cues from ASR transcripts and end-to-end segment selection, often neglecting the rich visual context and leading to incoherent outputs. In this paper, we propose a Human-Inspired automatic video editing framework (HIVE) that leverages multimodal narrative understanding to address these limitations. Our approach incorporates character extraction, dialogue analysis, and narrative summarization through multimodal large language models, enabling a holistic understanding of the video content. To further enhance coherence, we apply scene-level segmentation and decompose the editing process into three subtasks: highlight detection, opening/ending selection, and pruning of irrelevant content. To facilitate research in this area, we introduce DramaAD, a novel benchmark dataset comprising over 2500 short drama episodes and 500 professionally edited advertisement clips. Experimental results demonstrate that our framework consistently outperforms existing baselines across both general and advertisement-oriented editing tasks, significantly narrowing the quality gap between automatic and human-edited videos.
Efficiency-Effectiveness Reranking FLOPs for LLM-based Rerankers
Authors: Zhiyuan Peng; Ting-Ruen Wei; Tingyu Song; Yilun Zhao
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Large Language Models (LLMs) have recently been applied to reranking tasks in information retrieval, achieving strong performance. However, their high computational demands often hinder practical deployment.Existing studies evaluate the efficiency of LLM-based rerankers using proxy metrics such as latency, the number of forward passes, input tokens, and output tokens. However, these metrics depend on hardware and running-time choices (parallel or not, batch size, etc), and often fail to account for model size, making it difficult to interpret and obscuring the evaluation of the efficiency-effectiveness tradeoff. To address this issue, we propose for LLM-based rerankers: RPP (ranking metrics per PetaFLOP), measuring how much ranking quality (e.g., NDCG or MRR) a method achieves per PetaFLOP, and QPP (queries per PetaFLOP), measuring how many queries can be processed per PetaFLOP. Accompanied by the new metrics, an interpretable FLOPs estimator is developed to estimate the FLOPs of an LLM-based reranker even without running any experiments. Based on the proposed metrics, we conduct comprehensive experiments to evaluate a wide range of LLM-based rerankers with different architectures, studying the efficiency-effectiveness trade-off and bringing this issue to the attention of the research community.
GEAR: A Scalable and Interpretable Evaluation Framework for RAG-Based Car Assistant Systems
Authors: Niloufar Beyranvand; Hamidreza Dastmalchi; Aijun An; Heidar Davoudi; Winston Chan; Ron DiCarlantonio
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Large language models (LLMs) increasingly power car assistants, enabling natural language interaction for tasks such as maintenance, troubleshooting, and operational guidance. While retrieval-augmented generation (RAG) improves grounding using vehicle manuals, evaluating response quality remains a key challenge. Traditional metrics like BLEU and ROUGE fail to capture critical aspects such as factual accuracy and information coverage. We propose GEAR, a fully automated, reference-based evaluation framework for car assistant systems. GEAR uses LLMs as evaluators to compare assistant responses against ground-truth counterparts, assessing coverage, correctness, and other dimensions of answer quality. To enable fine-grained evaluation, both responses are decomposed into key facts and labeled as essential, optional, or safety-critical using LLMs. The evaluator then determines which of these facts are correct and covered. Experiments show that GEAR aligns closely with human annotations, offering a scalable and reliable solution for evaluating car assistants.
FQ-Eval: Building Evaluation Dataset for User-centered Follow-up Question Generation
Authors: Sanghyun Seo; Bumsoo Kang; Dahm Lee; Jaeheon Kim; Joongbo Shin; Eui Soon Kim; Kijeong Jeon
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: To effectively support users’ goal achievement in chat-LLM services, providing user-centered follow-up questions is essential. Existing studies primarily focus on enhancing information-seeking or topical relevance, often missing how follow-up questions could satisfy users’ intrinsic needs and conversational goals. To bridge this gap, we introduce FQ-Eval, a user-centered evaluation dataset designed for assessing follow-up question generation in chat-LLM services. FQ-Eval incorporates realistic chat-LLM usage scenarios and five distinct human-aligned criteria, each reflecting user expectations of effective follow-up questions. Experimental results show that FQ-Eval constructed through our approach clearly capture human-aligned criteria, enabling robust, human-aligned follow-up question generation evaluation of various models and services.
Evaluating AI for Finance: Is AI Credible at Assessing Investment Risk Appetite?
Authors: Divij Chawla; Ashita Bhutada; Duc Anh Do; Abhinav Raghunathan; Vinod Sp; Cathy Guo; Dar Win Liew; Prannaya Gupta; Rishabh Bhardwaj; Rajat Bhardwaj; Soujanya Poria
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: We assess whether AI systems can credibly evaluate investment risk appetite—a task that must be thoroughly validated before automation. Our analysis was conducted on proprietary systems (GPT, Claude, Gemini) and open-weight models (LLaMA, DeepSeek, Mistral), using carefully curated user profiles that reflect real users with varying attributes such as country and gender. As a result, the models exhibit significant variance in score distributions when user attributes—such as country or gender—that should not influence risk computation are changed. For example, GPT-4o assigns higher risk scores to Nigerian and Indonesian profiles. While some models align closely with expected scores in the low- and mid-risk ranges, none maintain consistent scores across regions and demographics, thereby violating AI and finance regulations.