GenAI4ELab/papercli-papers huggingface dataset:
Generating Spatial Knowledge Graphs from Automotive Diagrams for Question Answering
Authors: Steve Bakos; Chen Xing; Heidar Davoudi; Aijun An; Ron DiCarlantonio
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Answering “Where is the X button?” with “It’s next to the Y button” is unhelpful if the user knows neither location. Useful answers require obvious landmarks as a reference point. We address this by generating from a vehicle dashboard diagram a spatial knowledge graph (SKG) that shows the spatial relationship between a dashboard component and its nearby landmarks and using the SKG to help answer questions. We evaluate three distinct generation pipelines (Per-Attribute, Per-Component, and a Single-Shot baseline) to create the SKG using Large Vision-Language Models (LVLMs). On a new 65-vehicle dataset, we demonstrate that a decomposed Per-Component pipeline is the most effective strategy for generating a high-quality SKG; the graph produced by this method, when evaluated with a novel Significance score, identifies landmarks achieving 71.3% agreement with human annotators. This work enables downstream QA systems to provide more intuitive, landmark-based answers.
Enhancing Persuasive Dialogue Agents by Synthesizing Cross‐Disciplinary Communication Strategies
Authors: Shinnosuke Nozue; Yuto Nakano; Yotaro Watanabe; Meguru Takasaki; Shoji Moriya; Reina Akama; Jun Suzuki
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Current approaches to developing persuasive dialogue agents often rely on a limited set of predefined persuasive strategies that fail to capture the complexity of real-world interactions. We applied a cross-disciplinary approach to develop a framework for designing persuasive dialogue agents that draws on proven strategies from social psychology, behavioral economics, and communication theory. We validated our proposed framework through experiments on two distinct datasets: the Persuasion for Good dataset, which represents a specific in-domain scenario, and the DailyPersuasion dataset, which encompasses a wide range of scenarios. The proposed framework achieved strong results for both datasets and demonstrated notable improvement in the persuasion success rate as well as promising generalizability. Notably, the proposed framework also excelled at persuading individuals with initially low intent, which addresses a critical challenge for persuasive dialogue agents.
BIOPSY - Biomarkers In Oncology: Pipeline for Structured Yielding
Authors: Sanya A. Chetwani; Jaseem Mahmmdla
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: In clinical science, biomarkers are crucial indicators for early cancer detection, prognosis, and guiding personalized treatment decisions. Although critical, extracting biomarkers and their levels from clinical texts remains a complex and underexplored problem in natural language processing research. In this paper, we present BIOPSY, an end-to-end pipeline that integrates a domain-adapted biomarker entity recognition model, a relation extraction model to link biomarkers to their respective mutations, a biomarker-type classifier, and finally, a tailored algorithm to capture biomarker expression levels. Evaluated on 5,000 real-world clinical texts, our system achieved an overall F1 score of 0.86 for oncology and 0.87 for neuroscience domains. This reveals the ability of the pipeline to adapt across various clinical sources, including trial records, research papers, and medical notes, offering the first comprehensive solution for end-to-end, context-aware biomarker extraction and interpretation in clinical research.
DAST: Difficulty-Adaptive Slow-Thinking for Large Reasoning Models
Authors: Yi Shen; Jian Zhang; Jieyun Huang; Shuming Shi; Wenjing Zhang; Jiangze Yan; Ning Wang; Kai Wang; Zhaoxiang Liu; Shiguo Lian
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Recent advancements in slow-thinking reasoning models have shown exceptional performance in complex reasoning tasks. However, their tendency for “overthinking” on simple problems leads to excessive computational resource usage and increased inference latency, which hinders their widespread industrial adoption. While current mitigation strategies uniformly reduce reasoning tokens, they risk degrading performance on challenging tasks that require extended reasoning. This paper introduces Difficulty-Adaptive Slow-Thinking (DAST), a novel framework that enables models to autonomously adjust Chain-of-Thought (CoT) length based on problem difficulty. We propose a Token Length Budget (TLB) metric and leverage budget-aware preference optimization to implement DAST, which penalizes inefficiency on simple problems while incentivizing deep reasoning for complex ones. Experiments demonstrate DAST’s significant value for practical application: it effectively mitigates overthinking, substantially lowering costs and latency—while crucially preserving high accuracy on complex problems, paving the way for the efficient application of advanced reasoning models.
pEBR: A Probabilistic Approach to Embedding Based Retrieval
Authors: Han Zhang; Yunjiang Jiang; Mingming Li; Haowei Yuan; Yiming Qiu; Wen-Yun Yang
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Embedding-based retrieval aims to learn a shared semantic representation space for both queries and items, enabling efficient and effective item retrieval through approximate nearest neighbor (ANN) algorithms. In current industrial practice, retrieval systems typically retrieve a fixed number of items for each query. However, this fixed-size retrieval often results in insufficient recall for head queries and low precision for tail queries. This limitation largely stems from the dominance of frequentist approaches in loss function design, which fail to address this challenge in industry. In this paper, we propose a novel probabilistic Embedding-Based Retrieval (pEBR) framework. Our method models the item distribution conditioned on each query, enabling the use of a dynamic cosine similarity threshold derived from the cumulative distribution function (CDF) of the probabilistic model. Experimental results demonstrate that pEBR significantly improves both retrieval precision and recall. Furthermore, ablation studies reveal that the probabilistic formulation effectively captures the inherent differences between head-to-tail queries.
Finding Diamonds in Conversation Haystacks: A Benchmark for Conversational Data Retrieval
Authors: Yohan Lee; Yongwoo Song; Sangyeop Kim
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: We present the Conversational Data Retrieval (CDR) benchmark, the first comprehensive test set for evaluating systems that retrieve conversation data for product insights. With 1.6k queries across five analytical tasks and 9.1k conversations, our benchmark provides a reliable standard for measuring conversational data retrieval performance. Our evaluation of 16 popular embedding models shows that even the best models reach only around NDCG@10 of 0.51, revealing a substantial gap between document and conversational data retrieval capabilities. Our work identifies unique challenges in conversational data retrieval (implicit state recognition, turn dynamics, contextual references) while providing practical query templates and detailed error analysis across different task categories. The benchmark dataset and code are available at https://github.com/l-yohai/CDR-Benchmark.
<SYNTACT>: Structuring Your Natural Language SOPs into Tailored Ambiguity-Resolved Code Templates
Authors: Sachin Kumar Giroh; Pushpendu Ghosh; Aryan Jain; Harshal Giridhari Paunikar; Aditi Rastogi; Promod Yenigalla; Anish Nediyanchath
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: This paper introduces <SYNTACT>, a three-stage multi agent LLM framework designed to transform unstructured and ambiguous Standard Operating Procedure (SOP) into a structured plan and an executable code template. Unstructured SOPs—common across industries such as finance, retail, and logistics—frequently suffer from ambiguity, missing information, and inconsistency, all of which hinder automation. SYNTACT addresses this through: (1) a Clarifier module that disambiguate the SOP using large language models, internal knowledge base (RAG) and human-in-the-loop , (2) a Planner that converts refined natural language instructions into a structured plan of hierarchical task flows through function (API) tagging, conditional branches and human-in-the-loop check-points, and (3) an Implementor that generates executable code fragments or pseudocode templates. We evaluate SYNTACT on real-world SOPs and synthetic variants, demonstrating an 88.4% end-to-end accuracy and a significant reduction in inconsistency compared to leading LLM baselines. Ablation studies highlight the necessity of each component, with performance dropping notably when modules are removed.Our findings show that structured multi-agent pipelines like SYNTACT can meaningfully improve consistency, reduce manual effort, and accelerate automation at scale.
Recover-LoRA: Data-Free Accuracy Recovery of Degraded Language Models via Low-Rank Adaptation
Authors: Devleena Das; Rajeev Patwari; Ashish Sirasao
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Inference optimizations such as quantization, pruning, format and datatype conversion, model export, and serialization can lead to functional degradations in language model task performance. While most efforts on performance recovery for deployment focus on robust quantization techniques, we focus on recovering model accuracies from any sources that degrade model weights, such as improper model serialization. In this work, we propose Recover-LoRA, a lightweight and dataset agnostic method to recover accuracy in degraded models. Recover-LoRA uses synthetic data and logit distillation to learn LoRA adapters on selective layers that facilitate aligning the degraded model to its full precision model. We investigate the utility of Recover-LoRA across a diverse set of small language models (SLMs), including models with varying attention architectures, multi-head attention (MHA) and group-query attention (GQA), as well as several evaluation datasets. Our results show that Recover-LoRA recovers model accuracies by 5-17% on MHA and GQA SLMs.
ixi-GEN: Efficient Industrial sLLMs through Domain Adaptive Continual Pretraining
Authors: Seonwu Kim; Yohan Na; Kihun Kim; Hanhee Cho; Geun Lim; Mintae Kim; Seongik Park; Ki Hyun Kim; Youngsub Han; Byoung-Ki Jeon
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: The emergence of open-source large language models (LLMs) has expanded opportunities for enterprise applications; however, many organizations still lack the infrastructure to deploy and maintain large-scale models. As a result, small LLMs (sLLMs) have become a practical alternative despite inherent performance limitations. While Domain Adaptive Continual Pretraining (DACP) has been explored for domain adaptation, its utility in commercial settings remains under-examined. In this study, we validate the effectiveness of a DACP-based recipe across diverse foundation models and service domains, producing DACP-applied sLLMs (ixi-GEN). Through extensive experiments and real-world evaluations, we demonstrate that ixi-GEN models achieve substantial gains in target-domain performance while preserving general capabilities, offering a cost-efficient and scalable solution for enterprise-level deployment.
GRAFT: A Graph-based Flow-aware Agentic Framework for Document-level Machine Translation
Authors: Himanshu Dutta; Sunny Manchanda; Prakhar Bapat; Meva Ram Gurjar; Pushpak Bhattacharyya
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Enterprises, public organizations, and localization providers increasingly rely on Document-level Machine Translation (DocMT) to process contracts, reports, manuals, and multimedia transcripts across languages. However, existing MT systems often struggle to handle discourse-level phenomena such as pronoun resolution, lexical cohesion, and ellipsis, resulting in inconsistent or incoherent translations. We propose **GRAFT**, a modular graph-based DocMT framework that leverages Large Language Model (LLM) agents to segment documents into discourse units, infer inter-discourse dependencies, extract structured memory, and generate context-aware translations. GRAFT transforms documents into directed acyclic graphs (DAGs) to explicitly model translation flow and discourse structure. Experiments across eight language directions and six domains show GRAFT outperforms commercial systems (e.g., Google Translate) and closed LLMs (e.g., GPT-4) by an average of 2.8 d-BLEU, and improves terminology consistency and discourse handling. GRAFT supports deployment with open-source LLMs (e.g., LLaMA, Qwen), making it cost-effective and privacy-preserving. These results position GRAFT as a robust solution for scalable, document-level translation in real-world applications.
Recon, Answer, Verify: Agents in Search of Truth
Authors: Satyam Shukla; Himanshu Dutta; Pushpak Bhattacharyya
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Human fact-checking is too slow to meet current demands, making automatic fact-checking system an essential alternative. Evaluating such systems is challenging as existing benchmark datasets either suffer from leakage or evidence incompleteness. This limits the realism of current evaluations. We present Politi-Fact-Only (PFO), a 5-class benchmark dataset of 2,982 political claims from politifact.com, where all post-claim analysis and annotator cues have been removed manually from evidence article. After filtration, evidence contains information available prior to the claim’s verification. By evaluating PFO, we see an average performance drop of 11.39% in terms of macro-f1 compared to PFO’s unfiltered version. Based on the identified challenges of the existing LLM-based fact-checking system, we propose RAV (Recon-Answer-Verify), an agentic framework with three agents, it iteratively generates and answers sub-questions to verify different aspects of the claim before finally generating the label. Unlike prior literature, we worked on reducing the follow-up question complexity by leveraging two 2 types of structured questions, which either validate a fact or inquire about a fact. RAV generalizes across both domains and label granularities, outperforming state-of-the-art methods by 57.5% on PFO (political, 5-class) and by 3.05% on the widely used HOVER dataset (encyclopedic, 2-class).
T-VEC: A Telecom-Specific Vectorization Model with Enhanced Semantic Understanding via Deep Triplet Loss Fine-Tuning
Authors: Vignesh Ethiraj; Ashwath D; Sidhanth Menon; Divya Vijay; Vidhyakshaya Kannan
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: The specialized vocabulary and nuanced concepts of the telecommunications industry pose persistent challenges for standard Natural Language Processing (NLP) models. Generic embedding models often struggle to represent telecom-specific semantics, limiting their utility in retrieval and downstream tasks. We present T-VEC (Telecom Vectorization Model), a domain-adapted embedding model fine-tuned from the gte-Qwen2-1.5B-instruct backbone using a triplet loss objective. Fine-tuning was performed on T-Embed, a high-quality, large-scale dataset covering diverse telecom concepts, standards, and operational scenarios. Although T-Embed contains some proprietary material and cannot be fully released, we open source 75% of the dataset to support continued research in domain-specific representation learning. On a custom benchmark comprising 1500 query-passage pairs from IETF RFCs and vendor manuals, T-VEC surpasses MPNet, BGE, Jina and E5, demonstrating superior domain grounding and semantic precision in telecom-specific retrieval. Embedding visualizations further showcase tight clustering of telecom-relevant concepts. We release T-VEC and its tokenizer to support semantically faithful NLP applications within the telecom domain.
PlanGPT-VL: Enhancing Urban Planning with Domain-Specific Vision-Language Models
Authors: He Zhu; Junyou Su; Minxin Chen; Wen Wang; Yijie Deng; Guanhua Chen; Wenjia Zhang
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: In the field of urban planning, existing Vision-Language Models (VLMs) frequently fail to effectively analyze planning maps, which are critical for urban planners and educational contexts. Planning maps require specialized understanding of spatial configurations, regulatory requirements, and multi-scale analysis.To address this challenge, we introduce PlanGPT-VL, the first domain-specific VLM tailored for urban planning maps. PlanGPT-VL employs three innovations:(1) PlanAnno-V framework for high-quality VQA data synthesis,(2) Critical Point Thinking (CPT) to reduce hallucinations through structured verification, and(3) PlanBench-V benchmark for systematic evaluation.Evaluation on PlanBench-V shows that PlanGPT-VL outperforms general-purpose VLMs on planning map interpretation tasks, with our 7B model achieving performance comparable to larger 72B models.
IPR: Intelligent Prompt Routing with User-Controlled Quality-Cost Trade-offs
Authors: Aosong Feng; Balasubramaniam Srinivasan; Yun Zhou; Zhichao Xu; Kang Zhou; Sheng Guan; Yueyan Chen; Xian Wu; Ninad Kulkarni; Yi Zhang; Zhengyuan Shen; Dmitriy Bespalov; Soumya Smruti Mishra; Yifei Teng; Darren Yow-Bang Wang; Haibo Ding; Lin Lee Cheong
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Routing incoming queries to the most cost-effective LLM while maintaining response quality poses a fundamental challenge in optimizing performance-cost trade-offs for large-scale commercial systems.We present IPR—a quality-constrained Intelligent Prompt Routing framework that dynamically selects optimal models based on predicted response quality and user-specified tolerance levels.IPR introduces three key innovations: (1) a modular architecture with lightweight quality estimators trained on 1.5M prompts annotated with calibrated quality scores, enabling fine-grained quality prediction across model families; (2) a user-controlled routing mechanism with tolerance parameter 𝜏 ∈ [0,1] that provides explicit control over quality-cost trade-offs; and (3) an extensible design using frozen encoders with model-specific adapters, reducing new model integration from days to hours. To rigorously train and evaluate IPR, we curate an industrial-level IPR dataset, a comprehensive benchmark containing 1.5 million examples with response quality annotations across 11 LLM candidates.Deployed on a major cloud platform, IPR achieves 43.9% cost reduction while maintaining quality parity with the strongest model in the Claude family and processes requests with sub-150ms latency.
Semantic Agreement Enables Efficient Open-Ended LLM Cascades
Authors: Duncan Soiffer; Steven Kolawole; Virginia Smith
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Cascade systems for open-ended text generation face a fundamental challenge: determining output reliability when generation quality lies on a continuous spectrum, often with multiple valid responses. To address this, we propose _semantic agreement_—meaning-level consensus between ensemble outputs—as a training-free signal for reliable deferral. We show that when diverse model outputs agree semantically, their consensus is a stronger reliability signal than token-level confidence. Evaluated from 500M to 70B-parameter models, semantic cascades improve deferral accuracy, match or surpass target-model quality at 40% of the cost, and reduce latency by up to 60%. Our method requires no model internals, works across black-box APIs, and remains robust to model updates, making it a practical baseline for real-world LLM deployment.
Lost in Pronunciation: Detecting Chinese Offensive Language Disguised by Phonetic Cloaking Replacement
Authors: Haotan Guo; Jianfei He; Jiayuan Ma; Hongbin Na; Zimu Wang; Haiyang Zhang; Qi Chen; Wei Wang; Zijing Shi; Tao Shen; Ling Chen
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Phonetic Cloaking Replacement (PCR), defined as the deliberate use of homophonic or near-homophonic variants to hide toxic intent, has become a major obstacle to Chinese content moderation. While this problem is well-recognized, existing evaluations predominantly rely on rule-based, synthetic perturbations that ignore the creativity of real users. We organize PCR into a four-way surface-form taxonomy and compile PCR-ToxiCN, a dataset of 500 naturally occurring, phonetically cloaked offensive posts gathered from the RedNote platform. Benchmarking state-of-the-art LLMs on this dataset exposes a serious weakness: the best model reaches only an F1-score of 0.672, and zero-shot chain-of-thought prompting pushes performance even lower. Guided by error analysis, we revisit a Pinyin-based prompting strategy that earlier studies judged ineffective and show that it recovers much of the lost accuracy. This study offers the first comprehensive taxonomy of Chinese PCR, a realistic benchmark that reveals current detectors’ limits, and a lightweight mitigation technique that advances research on robust toxicity detection.
Distilling Cross-Modal Knowledge into Domain-Specific Retrievers for Enhanced Industrial Document Understanding
Authors: Jinhyeong Lim; Jeongwan Shin; Seeun Lee; Seongdeok Kim; Joungsu Choi; Jongbae Kim; Chun Hwan Jung; Youjin Kang
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Retrieval-Augmented Generation (RAG) has shown strong performance in open-domain tasks, but its effectiveness in industrial domains is limited by a lack of domain understanding and document structural elements (DSE) such as tables, figures, charts, and formula.To address this challenge, we propose an efficient knowledge distillation framework that transfers complementary knowledge from both Large Language Models (LLMs) and Vision-Language Models (VLMs) into a compact domain-specific retriever.Extensive experiments and analysis on real-world industrial datasets from shipbuilding and electrical equipment domains demonstrate that the proposed framework improves both domain understanding and visual-structural retrieval, outperforming larger baselines while requiring significantly less computational complexity.