GenAI4ELab/papercli-papers huggingface dataset:
LLMInit: A Free Lunch from Large Language Models for Selective Initialization of Recommendation
Authors: Weizhi Zhang; Liangwei Yang; Wooseong Yang; Henry Peng Zou; Yuqing Liu; Ke Xu; Sourav Medya; Philip S. Yu
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Collaborative filtering (CF) is widely adopted in industrial recommender systems (RecSys) for modeling user-item interactions across numerous applications, but often struggles with cold-start and data-sparse scenarios. Recent advancements in pre-trained large language models (LLMs) with rich semantic knowledge, offer promising solutions to these challenges. However, deploying LLMs at scale is hindered by their significant computational demands and latency. In this paper, we propose a novel and scalable LLM-RecSys framework, LLMInit, designed to integrate pretrained LLM embeddings into CF models through selective initialization strategies. Specifically, we identify the embedding collapse issue observed when CF models scale and match the large embedding sizes in LLMs and avoid the problem by introducing efficient sampling methods, including, random, uniform, and variance-based selections. Comprehensive experiments conducted on multiple real-world datasets demonstrate that LLMInit significantly improves recommendation performance while maintaining low computational costs, offering a practical and scalable solution for industrial applications. To facilitate industry adoption and promote future research, we provide open-source access to our implementation at https://github.com/DavidZWZ/LLMInit.
LLM Agents Implement an NLG System from Scratch: Building Interpretable Rule-Based RDF-to-Text Generators
Authors: Mateusz Lango; Ondrej Dusek
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: We present a novel neurosymbolic framework for RDF-to-text generation, in which the model is “trained” through collaborative interactions among multiple LLM agents rather than traditional backpropagation. The LLM agents produce rule-based Python code for a generator for the given domain, based on RDF triples only, with no in-domain human reference texts. The resulting system is fully interpretable, requires no supervised training data, and generates text nearly instantaneously using only a single CPU. Our experiments on the WebNLG and OpenDialKG data show that outputs produced by our approach reduce hallucination, with only slight fluency penalties compared to finetuned or prompted language models.
Leveraging LLMs to Streamline the Review of Public Funding Applications
Authors: João DS Marques; Andre Vicente Duarte; André Mendes Marques de Carvalho; Gil Rocha; Bruno Martins; Arlindo L. Oliveira
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Every year, the European Union and its member states allocate millions of euros to fund various development initiatives. However, the increasing number of applications received for these programs often creates significant bottlenecks in evaluation processes, due to limited human capacity. In this work, we detail the real-world deployment of AI-assisted evaluation within the pipeline of two government initiatives: (i) corporate applications aimed at international business expansion, and (ii) citizen reimbursement claims for investments in energy-efficient home improvements. While these two cases involve distinct evaluation procedures, our findings confirm that AI effectively enhanced processing efficiency and reduced workload across both types of applications. Specifically, in the citizen reimbursement claims initiative, our solution increased reviewer productivity by 20.1%, while keeping a negligible false-positive rate based on our test set observations. These improvements resulted in an overall reduction of more than 2 months in the total evaluation time, illustrating the impact of AI-driven automation in large-scale evaluation workflows.
AMAS: Adaptively Determining Communication Topology for LLM-based Multi-agent System
Authors: Hui Yi Leong; Yuheng Li; Yuqing Wu; Wenwen Ouyang; Wei Zhu; Jiechao Gao; Wei Han
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Although large language models (LLMs) have revolutionized natural language processing capabilities, their practical implementation as autonomous multi-agent systems (MAS) for industrial problem-solving encounters persistent barriers. Conventional MAS architectures are fundamentally restricted by inflexible, hand-crafted graph topologies that lack contextual responsiveness, resulting in diminished efficacy across varied academic and commercial workloads. To surmount these constraints, we introduce AMAS, a paradigm-shifting framework that redefines LLM-based MAS through a novel dynamic graph selector. This component autonomously identifies task-specific optimal graph configurations via lightweight LLM adaptation, eliminating the reliance on monolithic, universally applied structural templates. Instead, AMAS exploits the intrinsic properties of individual inputs to intelligently direct query trajectories through task-optimized agent pathways. Rigorous validation across question answering, mathematical deduction, and code generation benchmarks confirms that AMAS systematically exceeds state-of-the-art single-agent and multi-agent approaches across diverse LLM architectures. Our investigation establishes that context-sensitive structural adaptability constitutes a foundational requirement for high-performance LLM MAS deployments.
ColMate: Contrastive Late Interaction and Masked Text for Multimodal Document Retrieval
Authors: Ahmed Masry; Megh Thakkar; Patrice Bechard; Sathwik Tejaswi Madhusudhan; Rabiul Awal; Shambhavi Mishra; Akshay Kalkunte Suresh; Srivatsava Daruru; Enamul Hoque; Spandana Gella; Torsten Scholak; Sai Rajeswar
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Retrieval-augmented generation has proven practical when models require specialized knowledge or access to the latest data. However, existing methods for multimodal document retrieval often replicate techniques developed for text-only retrieval, whether in how they encode documents, define training objectives, or compute similarity scores. To address these limitations, we present ColMate, a document retrieval model that bridges the gap between multimodal representation learning and document retrieval. ColMate utilizes a novel OCR-based pretraining objective, a self-supervised masked contrastive learning objective, and a late interaction scoring mechanism more relevant to multimodal document structures and visual characteristics. ColMate obtains 3.61% improvements over existing retrieval models on the ViDoRe V2 benchmark, demonstrating stronger generalization to out-of-domain benchmarks.
Confidence-Aware Reasoning: Optimizing Self-Guided Thinking Trajectories in Large Reasoning Models
Authors: Jiaxin Zhang
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Chain-of-thought enables large reasoning models (LRMs) to reason through multi-step problems but often leads to unnecessarily long or redundant reasoning traces, a phenomenon known as overthinking. This results in inflated inference costs and potential degradation in answer quality. To address these challenges, we propose Confidence-Aware Reasoning (), an inference-time framework that optimizes reasoning trajectories by selectively pruning low-utility reasoning blocks and halting early when sufficient confidence has been achieved. is theoretically grounded in Bayesian optimal experimental design, treating each reasoning block as a sequential decision whose utility is approximated by its marginal contribution to reducing final answer uncertainty. We introduce a lightweight implementation that leverages token-level confidence to dynamically modulate reasoning depth without additional supervision. Evaluations on multiple benchmarks, including AMC, AIME, GPQA-Diamond, and MATH-500 show that improves answer accuracy by up to +13.3%, while reducing average reasoning length by 40%–50%. Our findings demonstrate that information-theoretic insights can effectively control self-guided reasoning and enable LRMs to “think just enough” at test time.
Multi-Value-Product Retrieval-Augmented Generation for Industrial Product Attribute Value Identification
Authors: Huike Zou; Haiyang Yang; Yindu Su; Chen Li Yu; Qinye Xie; Chengbao Lian; Qingheng Zhang; Shuguang Han; Fei Huang; Jufeng Chen
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Identifying attribute values from product profiles is a key task for improving product search, recommendation, and business analytics on e-commerce platforms, which we called Product Attribute Value Identification (PAVI) . However, existing PAVI methods face critical challenges, such as cascading errors, inability to handle out-of-distribution (OOD) attribute values, and lack of generalization capability. To address these limitations, we introduce Multi-Value-Product Retrieval-Augmented Generation (MVP-RAG), combining the strengths of retrieval, generation, and classification paradigms. MVP-RAG defines PAVI as a retrieval-generation task, where the product title description serves as the query, and products and attribute values act as the corpus. It first retrieves similar products of the same category and candidate attribute values, and then generates the standardized attribute values. The key advantages of this work are: (1) the proposal of a multi-level retrieval scheme, with products and attribute values as distinct hierarchical levels in PAVI domain (2) attribute value generation of large language model to significantly alleviate the OOD problem and (3) its successful deployment in a real-world industrial environment. Extensive experimental results on the dataset demonstrate that the proposed method performs better than the state-of-the-art baselines.
AttributeForge: An Agentic LLM Framework for Automated Product Schema Modeling
Authors: Yunhan Huang; Klevis Ramo; Andrea Iovine; Melvin Monteiro; Sedat Gokalp; Arjun Bakshi; Hasan Turalic; Arsh Kumar; Jona Neumeier; Ripley Yates; Rejaul Monir; Simon Hartmann; Tushar Manglik; Mohamed Yakout
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Effective product schema modeling is fundamental to e-commerce success, enabling accurate product discovery and superior customer experience. However, traditional manual schema modeling processes are severely bottlenecked, producing only tens of attributes per month, which is insufficient for modern e-commerce platforms managing thousands of product types. This paper introduces AttributeForge, the first framework to automate end-to-end product schema modeling using Large Language Models (LLMs). Our key innovation lies in orchestrating 43 specialized LLM agents through strategic workflow patterns to handle the complex interdependencies in schema generation. The framework incorporates two novel components: MC2-Eval, a comprehensive validation system that assesses schemas against technical, business, and customer experience requirements; and AutoFix, an intelligent mechanism that automatically corrects modeling defects through iterative refinement. Deployed in production, AttributeForge achieves an 88× increase in modeling throughput while delivering superior quality: a 59.83% Good-to-Good (G2G) conversion rate compared to 37.50% for manual approaches. This significant improvement in both speed and quality enables e-commerce platforms to rapidly adapt their product schemas to evolving market needs.
VestaBench: An Embodied Benchmark for Safe Long-Horizon Planning Under Multi-Constraint and Adversarial Settings
Authors: Tanmana Sadhu; Yanan Chen; Ali Pesaranghader
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Large language models (LLMs) are applied to reasoning and (automated) planning across diverse domains, from travel itineraries to embodied AI tasks. However, concerns have been raised about their suitability for long-horizon tasks involving multiple constraints, as they are prone to hallucinations, particularly in adversarial scenarios. Safety reasoning also becomes critical for embodied AI agents, which interact with their physical environments to complete tasks on behalf of humans. However, existing (safety) benchmarks fail to represent a diverse range of multi-constraint tasks that require long-horizon planning with a focus on safety. To address this, we propose VESTABENCH, a benchmark curated using VirtualHome and BEHAVIOR-100. Our VESTABENCH includes (1) tasks that can be achieved safely under adversarial and multi-constraint settings, as well as (2) adversarial instructions that the agent must avoid. Our experiments with state-of-the-art LLM-based baselines reveal that they perform poorly against our tasks, not only achieving low success rates but also suffering significantly compromised safety outcomes. This observation reinforces the limitations of LLMs in generating safe plans when faced with adversarial settings or instructions. Finally, we believe that our findings benefit the research and industry communities.
Advancing E-commerce Merchants Telemarketing with Synthetic Data-Driven LLMs
Authors: Qi Gou; Zehua Xia; Li Juan; Qingyang Zhao; Wenjing Yang
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Telemarketing towards merchants is considerably more complex than traditional dialogue systems. Given a user utterance, the response must not only follow the context but also strategically and naturally guide the conversation toward marketing objectives. A common approach is to fine-tune LLMs using high-quality dialogue data from top sales. However, we find that even after careful data cleaning, these data cannot be used directly due to two issues:(1) Poor strategy-following: Real-world conversations are highly random with much chit-chat topics, easily leading deviation from intended strategy.(2) Insufficient expert knowledge learning: Expert knowledge appears infrequently or not at all in limited collected corpus.To this end, we introduce a hybrid data synthesis framework with two main innovations. First, we unify the input schema with profile and strategy designed by top sales, and extract them via a Multi-task paradigm.In addition, we propose Role-playing Simulation and Session Prefix Completion to complementarily improve the strategy-following and inject long-tail expert knowledge into our fine-tuned model – TeleBot.Comprehensive online and offline evaluations demonstrate its effectiveness.In particular, in terms of the final marketing results – High Intention Rate, TeleBot reaches the performance level of the top 25% of human sales.
Depression Detection on Social Media with Large Language Models
Authors: Xiaochong Lan; Zhiguang Han; Yiming Cheng; Li Sheng; Jie Feng; Chen Gao; Yong Li
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Limited access to mental healthcare resources hinders timely depression diagnosis, leading to detrimental outcomes.Social media platforms present a valuable data source for early detection, yet this task faces two significant challenges: 1) the need for medical knowledge to distinguish clinical depression from transient mood changes, and 2) the dual requirement for high accuracy and model explainability.To address this, we propose DORIS, a framework that leverages Large Language Models (LLMs).To integrate medical knowledge, DORIS utilizes LLMs to annotate user texts against established medical diagnostic criteria and to summarize historical posts into temporal mood courses.These medically-informed features are then used to train an accurate Gradient Boosting Tree (GBT) classifier.Explainability is achieved by generating justifications for predictions based on the LLM-derived symptom annotations and mood course analyses.Extensive experimental results validate the effectiveness as well as interpretability of our method, highlighting its potential as a supportive clinical tool.
BullyBench: Youth & Experts-in-the-loop Framework for Intrinsic and Extrinsic Cyberbullying NLP Benchmarking
Authors: Kanishk Verma; Sri Balaaji Natarajan Kalaivendan; Arefeh Kazemi; Joachim Wagner; Darragh McCashin; Isobel Walsh; Sayani Basak; Sinan Asci; Yelena Cherkasova; Alexandrous Poullis; James O’Higgins Norman; Rebecca Umbach; Tijana Milosevic; Brian Davis
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Cyberbullying (CB) involves complex relational dynamics that are often oversimplified as a binary classification task. Existing youth-focused CB datasets rely on scripted role-play, lacking conversational realism and ethical youth involvement, with little or no evaluation of their social plausibility. To address this, we introduce a youth-in-the-loop dataset “BullyBench” developed by adolescents (ages 15–16) through an ethical co-research framework. We introduce a structured intrinsic quality evaluation with experts-in-the-loop (social scientists, psychologists, and content moderators) for assessing realism, relevance, and coherence in youth CB data. Additionally, we perform extrinsic baseline evaluation of this dataset by benchmarking encoder- and decoder-only language models for multi-class CB role classification for future research. A three-stage annotation process by young adults refines the dataset into a gold-standard test benchmark, a high-quality resource grounded in minors’ lived experiences of CB detection. Code and data are available for review
Tagging-Augmented Generation: Assisting Language Models in Finding Intricate Knowledge In Long Contexts
Authors: Anwesan Pal; Karen Hovsepian; Tinghao Guo; Mengnan Zhao; Somendra Tripathi; Nikos Kanakaris; George Mihaila; Sumit Nigam
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Recent investigations into effective context lengths of modern flagship large language models (LLMs) have revealed major limitations in effective question answering (QA) and reasoning over long and complex contexts for even the largest and most impressive cadre of models. While approaches like retrieval-augmented generation (RAG) and chunk-based re-ranking attempt to mitigate this issue, they are sensitive to chunking, embedding and retrieval strategies and models, and furthermore, rely on extensive pre-processing, knowledge acquisition and indexing steps. In this paper, we propose Tagging-Augmented Generation (TAG), a lightweight data augmentation strategy that boosts LLM performance in long-context scenarios, without degrading and altering the integrity and composition of retrieved documents. We validate our hypothesis by augmenting two challenging and directly relevant question-answering benchmarks – NoLima and NovelQA – and show that tagging the context or even just adding tag definitions into QA prompts leads to consistent relative performance gains over the baseline – up to 17% for 32K token contexts, and 2.9% in complex reasoning question-answering for multi-hop queries requiring knowledge across a wide span of text.
DispatchQA: A Benchmark for Small Function Calling Language Models in E-Commerce Applications
Authors: Joachim Daiber; Victor Maricato; Ayan Sinha; Andrew Rabinovich
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: We introduce DispatchQA, a benchmark to evaluate how well small language models (SLMs) translate open‐ended search queries into executable API calls via explicit function calling. Our benchmark focuses on the latency-sensitive e-commerce setting and measures SLMs’ impact on both search relevance and search latency. We provide strong, replicable baselines based on Llama 3.1 8B Instruct fine-tuned on synthetically generated data and find that fine-tuned SLMs produce search quality comparable or better than large language models such as GPT-4o while achieving up to 3× faster inference. All data, code, and training checkpoints are publicly released to spur further research on resource‐efficient query understanding.
Generalized Embedding Models for Industry 4.0 Applications
Authors: Christodoulos Constantinides; Shuxin Lin; Dhaval C Patel
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: In this work, we present the first embedding model specifically designed for Industry 4.0 applications, targeting the semantics of industrial asset operations. Given natural language tasks related to specific assets, our model retrieves relevant items and generalizes to queries involving similar assets, such as identifying sensors relevant to an asset’s failure mode. We systematically construct nine asset-specific datasets using an expert-validated knowledge base reflecting real operational scenarios. To ensure contextually rich embeddings, we augment queries with Large Language Models, generating concise entity descriptions that capture domain-specific nuances. Across five embedding models ranging from BERT (110M) to gte-Qwen (7B), we observe substantial in-domain gains: HIT@1 +54.2%, MAP@100 +50.1%, NDCG@10 +54.7% on average. Ablation studies reveal that (a) LLM-based query augmentation significantly improves embedding quality; (b) contrastive objectives without in-batch negatives are more effective for tasks with many relevant items; and (c) balancing positives and negatives in batches is essential. We evaluate on a new task and finally present a case study wrapping them as tools and providing them to a planning agent.
ECHO-LLaMA: Efficient Caching for High-Performance LLaMA Training
Authors: Maryam Dialameh; Rezaul Karim; Hossein Rajabzadeh; Omar Mohamed Awad; Boxing Chen; Hyock Ju Kwon; Walid Ahmed; Yang Liu
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: This paper introduces ECHO-LLaMA, an efficient LLaMA architecture designed to improve both the training speed and inference throughput of LLaMA architectures while maintaining its learning capacity. ECHO-LLaMA transforms LLaMA models into shared KV caching across certain layers, significantly reducing KV computational complexity while maintaining or improving language performance. Experimental results demonstrate that ECHO-LLaMA achieves up to 77% higher token-per-second throughput during training, up to 16% higher Model FLOPs Utilization (MFU), and up to 14% lower loss when trained on an equal number of tokens. Furthermore, on the 1.1B model, ECHO-LLaMA delivers approximately 7% higher test-time throughput compared to the baseline. By introducing a computationally efficient adaptation mechanism, ECHO-LLaMA offers a scalable and cost-effective solution for pretraining and finetuning large language models, enabling faster and more resource-efficient training without compromising performance.