GenAI4ELab/papercli-papers huggingface dataset:
Proactive Guidance of Multi-Turn Conversation in Industrial Search
Authors: Xiaoyu Li; Xiao Li; Li Gao; Yiding Liu; Xiaoyang Wang; Shuaiqiang Wang; Junfeng Wang; Dawei Yin
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track))

Abstract: The evolution of Large Language Models (LLMs) has significantly advanced multi-turn conversation systems, emphasizing the need for proactive guidance to enhance users’ interactions. However, these systems face challenges in dynamically adapting to shifts in users’ goals and maintaining low latency for real-time interactions. In the Baidu Search AI assistant, an industrial-scale multi-turn search system, we propose a novel two-phase framework to provide proactive guidance. The first phase, Goal-adaptive Supervised Fine-Tuning (G-SFT), employs a goal adaptation agent that dynamically adapts to user goal shifts and provides goal-relevant contextual information. G-SFT also incorporates scalable knowledge transfer to distill insights from LLMs into a lightweight model for real-time interaction. The second phase, Click-oriented Reinforcement Learning (C-RL), adopts a generate-rank paradigm, systematically constructs preference pairs from user click signals, and proactively improves click-through rates through more engaging guidance. This dual-phase architecture achieves complementary objectives: G-SFT ensures accurate goal tracking, while C-RL optimizes interaction quality through click signal-driven reinforcement learning. Extensive experiments demonstrate that our framework achieves 86.10% accuracy in offline evaluation (+23.95% over baseline) and 25.28% CTR in online deployment (149.06% relative improvement), while reducing inference latency by 69.55% through scalable knowledge distillation.
SpeechWeave: Diverse Multilingual Synthetic Text & Audio Data Generation Pipeline for Training Text to Speech Models
Authors: Karan Dua; Puneet Mittal; Ranjeet Gupta; Hitesh Laxmichand Patel
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track))

Abstract: High-quality Text-to-Speech (TTS) model training requires extensive and diverse text and speech data. It is challenging to procure such data from real sources due to issues of domain specificity, licensing, and scalability. Large language models (LLMs) can certainly generate textual data, but they create repetitive text with insufficient variation in the prompt during the generation process. Another important aspect in TTS training data is text normalization. Tools for normalization might occasionally introduce anomalies or overlook valuable patterns, and thus impact data quality. Furthermore, it is also impractical to rely on voice artists for large scale speech recording in commercial TTS systems with standardized voices. To address these challenges, we propose SpeechWeave, a synthetic speech data generation pipeline that is capable of automating the generation of multilingual, domain-specific datasets for training TTS models. Our experiments reveal that our pipeline generates data that is 10–48% more diverse than the baseline across various linguistic and phonetic metrics, along with speaker-standardized speech audio while generating approximately 97% correctly normalized text. Our approach enables scalable, high-quality data generation for TTS training, improving diversity, normalization, and voice consistency in the generated datasets.
Privacy Preserving Data Selection for Bias Mitigation in Speech Models
Authors: Alkis Koudounas; Eliana Pastor; Vittorio Mazzia; Manuel Giollo; Thomas Gueudre; Elisa Reale; Luca Cagliero; Sandro Cumani; Luca De Alfaro; Elena Baralis; Daniele Amberti
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track))

Abstract: Effectively selecting data from subgroups where a model performs poorly is crucial for improving its performance. Traditional methods for identifying these subgroups often rely on sensitive information, raising privacy issues. Additionally, gathering such information at runtime might be impractical. This paper introduces a cost-effective strategy that addresses these concerns. We identify underperforming subgroups and train a model to predict if an utterance belongs to these subgroups without needing sensitive information. This model helps mitigate bias by selecting and adding new data, which is labeled as challenging, for re-training the speech model. Experimental results on intent classification and automatic speech recognition tasks show the effectiveness of our approach in reducing biases and enhancing performance, with improvements in reducing error rates of up to 39% for FSC, 16% for ITALIC, and 22% for LibriSpeech.
ComRAG: Retrieval-Augmented Generation with Dynamic Vector Stores for Real-time Community Question Answering in Industry
Authors: Qinwen Chen; Wenbiao Tao; Zhiwei Zhu; Mingfan Xi; Liangzhong Guo; Yuan Wang; Wei Wang; Yunshi Lan
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track))

Abstract: Community Question Answering (CQA) platforms can be deemed as important knowledge bases in community, but effectively leveraging historical interactions and domain knowledge in real-time remains a challenge. Existing methods often underutilize external knowledge, fail to incorporate dynamic historical QA context, or lack memory mechanisms suited for industrial deployment. We propose ComRAG, a retrieval-augmented generation framework for real-time industrial CQA that integrates static knowledge with dynamic historical QA pairs via a centroid-based memory mechanism designed for retrieval, generation, and efficient storage. Evaluated on three industrial CQA datasets, ComRAG consistently outperforms all baselines—achieving up to 25.9% improvement in vector similarity, reducing latency by 8.7%–23.3%, and lowering chunk growth from 20.23% to 2.06% over iterations.
PlanGPT: Enhancing Urban Planning with a Tailored Agent Framework
Authors: He Zhu; Guanhua Chen; Wenjia Zhang
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track))

Abstract: In the field of urban planning, general-purpose large language models often struggle to meet the specific needs of planners. Tasks like generating urban planning texts, retrieving related information, and evaluating planning documents pose unique challenges. To enhance the efficiency of urban professionals and overcome these obstacles, we introduce PlanGPT, the first specialized AI agent framework tailored for urban and spatial planning. Developed through collaborative efforts with professional urban planners, PlanGPT integrates a customized local database retrieval system, domain-specific knowledge activation capabilities, and advanced tool orchestration mechanisms. Through its comprehensive agent architecture, PlanGPT coordinates multiple specialized components to deliver intelligent assistance precisely tailored to the intricacies of urban planning workflows. Empirical tests demonstrate that PlanGPT framework has achieved advanced performance, providing comprehensive support that significantly enhances professional planning efficiency.
FoodTaxo: Generating Food Taxonomies with Large Language Models
Authors: Pascal Wullschleger; Majid Zarharan; Donnacha Daly; Marc Pouly; Jennifer Foster
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track))

Abstract: We investigate the utility of Large Language Models for automated taxonomy generation and completion specifically applied to taxonomies from the food technology industry. We explore the extent to which taxonomies can be completed from a seed taxonomy or generated without a seed from a set of known concepts, in an iterative fashion using recent prompting techniques.Experiments on five taxonomies using an open-source LLM (Llama-3), while promising, point to the difficulty of correctly placing inner nodes.
Enriching children’s stories with LLMs: Delivering multilingual data enrichment for children’s books at scale and across markets
Authors: Zarah Weiss; Christof Meyer; Mikael Andersson
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track))

Abstract: This paper presents a user-centered, empirically guided approach to multilingual metadata enrichment for children’s books. We combine LLMs with human-in-the-loop quality control in a scalable CI/CD pipeline to curate brand collections that enhance book discovery and engagement for young readers across multiple European markets. Our results demonstrate that this hybrid approach delivers high-quality, child-appropriate labels, improves user experience, and accelerates deployment in real-world production environments. This work offers practical insights for applying generative NLP in the media and publishing industry.
Advanced Messaging Platform (AMP): Pipeline for Automated Enterprise Email Processing
Authors: Simerjot Kaur; Charese Smiley; Keshav Ramani; Elena Kochkina; Mathieu Sibue; Samuel Mensah; Pietro Totis; Cecilia Tilli; Toyin Aguda; Daniel Borrajo; Manuela Veloso
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track))

Abstract: Understanding and effectively responding to email communication remains a critical yet complex challenge for current AI techniques, especially in corporate environments. These tasks are further complicated by the need for domain-specific knowledge, accurate entity recognition, and high precision to prevent costly errors. While recent advances in AI, specifically Large Language Models (LLMs), have made strides in natural language understanding, they often lack business-specific expertise required in such settings. In this work, we present Advanced Messaging Platform (AMP), a production-grade AI pipeline that automates email response generation at scale in real-world enterprise settings. AMP has been in production for more than a year, processing thousands of emails daily while maintaining high accuracy and adaptability to evolving business needs.
Semantic Outlier Removal with Embedding Models and LLMs
Authors: Eren Akbiyik; João F. M. De Almeida; Rik Melis; Ritu Sriram; Viviana Petrescu; Vilhjálmur Vilhjálmsson
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track))

Abstract: Modern text processing pipelines demand robust methods to remove extraneous content while preserving a document’s core message. Traditional approaches—such as HTML boilerplate extraction or keyword filters—often fail in multilingual settings and struggle with context-sensitive nuances, whereas Large Language Models (LLMs) offer improved quality at high computational cost. We introduce SORE (Semantic Outlier Removal), a cost-effective, transparent method that leverages multilingual sentence embeddings and approximate nearest-neighbor search to identify and excise unwanted text segments. By first identifying core content via metadata embedding and then flagging segments that either closely match predefined outlier groups or deviate significantly from the core, SORE achieves near-LLM extraction precision at a fraction of the cost. Experiments on HTML datasets demonstrate that SORE outperforms structural methods and yield high precision in diverse scenarios. Our system is currently deployed in production, processing millions of documents daily across multiple languages while maintaining both efficiency and accuracy. To facilitate reproducibility and further research, we will publicly release our implementation and evaluation datasets.
SLENDER: Structured Outputs for SLM-based NER in Low-Resource Englishes
Authors: Nicole Ren; James Teo
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track))

Abstract: Named Entity Recognition (NER) for low-resource variants of English remains challenging, as most NER models are trained on datasets predominantly focused on American or British English. While recent work has shown that proprietary Large Language Models (LLMs) can perform NER effectively in low-resource settings through in-context learning, practical deployment is limited by their high computational costs and privacy concerns. Open-source Small Language Models (SLMs) offer promising alternatives, but the tendency of these Language Models (LM) to hallucinate poses challenges for production use. To address this, we introduce SLENDER, a novel output format for LM-based NER that achieves a three-fold reduction in inference time on average compared to JSON format, which is widely used for structured outputs. Our approach using Gemma-2-9B-it with the SLENDER output format and constrained decoding in zero-shot settings outperforms the en_core_web_trf model from SpaCy, an industry-standard NER tool, in all five regions of the Worldwide test set.
A Large-Scale Real-World Evaluation of an LLM-Based Virtual Teaching Assistant
Authors: Sunjun Kweon; Sooyohn Nam; Hyunseung Lim; Hwajung Hong; Edward Choi
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track))

Abstract: Virtual Teaching Assistants (VTAs) powered by Large Language Models (LLMs) have the potential to enhance student learning by providing instant feedback and facilitating multi-turn interactions. However, empirical studies on their effectiveness and acceptance in real-world classrooms are limited, leaving their practical impact uncertain. In this study, we develop an LLM-based VTA and deploy it in an introductory AI programming course with 477 graduate students. To assess how student perceptions of the VTA’s performance evolve over time, we conduct three rounds of comprehensive surveys at different stages of the course. Additionally, we analyze 3,869 student–VTA interaction pairs to identify common question types and engagement patterns. We then compare these interactions with traditional student-human instructor interactions to evaluate the VTA’s role in the learning process. Through a large-scale empirical study and interaction analysis, we assess the feasibility of deploying VTAs in real-world classrooms and identify key challenges for broader adoption. Finally, we release the source code of our VTA system, fostering future advancements in AI-driven education.
Operational Advice for Dense and Sparse Retrievers: HNSW, Flat, or Inverted Indexes?
Authors: Jimmy Lin
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track))

Abstract: Practitioners working on dense retrieval today face a bewildering number of choices. Beyond selecting the embedding model, another consequential choice is the actual implementation of nearest-neighbor vector search. While best practices recommend HNSW indexes, flat vector indexes with brute-force search represent another viable option, particularly for smaller corpora and for rapid prototyping. In this paper, we provide experimental results on the BEIR dataset using the open-source Lucene search library that explicate the tradeoffs between HNSW and flat indexes (including quantized variants) from the perspectives of indexing time, query evaluation performance, and retrieval quality. With additional comparisons between dense and sparse retrievers, our results provide guidance for today’s search practitioner in understanding the design space of dense and sparse retrievers. To our knowledge, we are the first to provide operational advice supported by empirical experiments in this regard.
Filter-And-Refine: A MLLM Based Cascade System for Industrial-Scale Video Content Moderation
Authors: Zixuan Wang; Jinghao Shi; Hanzhong Liang; Xiang Shen; Vera Wen; Zhiqian Chen; Yifan Wu; Zhixin Zhang; Hongyu Xiong
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track))

Abstract: Effective content moderation is essential for video platforms to safeguard user experience and uphold community standards. While traditional video classification models effectively handle well-defined moderation tasks, they struggle with complicated scenarios such as implicit harmful content and contextual ambiguity. Multimodal large language models (MLLMs) offer a promising solution to these limitations with their superior cross-modal reasoning and contextual understanding. However, two key challenges hinder their industrial adoption. First, the high computational cost of MLLMs makes full-scale deployment impractical. Second, adapting generative models for discriminative classification remains an open research problem. In this paper, we first introduce an efficient method to transform a generative MLLM into a multimodal classifier using minimal discriminative training data. To enable industry-scale deployment, we then propose a router-ranking cascade system that integrates MLLMs with a lightweight router model. Offline experiments demonstrate that our MLLM-based approach improves F1 score by 66.50% over traditional classifiers while requiring only 2% of the fine-tuning data. Online evaluations show that our system increases automatic content moderation volume by 41%, while the cascading deployment reduces computational cost to only 1.5% of direct full-scale deployment.
ASK: Aspects and Retrieval based Hybrid Clarification in Task Oriented Dialogue Systems
Authors: Rishav Sahay; Lavanya Sita Tekumalla; Purav Aggarwal; Arihant Jain; Anoop Saladi
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track))

Abstract: Ambiguous user queries pose a significant challenge in task-oriented dialogue systems relying on information retrieval. While Large Language Models (LLMs) have shown promise in generating clarification questions to tackle query ambiguity, they rely solely on the top-k retrieved documents for clarification which fails when ambiguity is too high to retrieve relevant documents in the first place. Traditional approaches lack principled mechanisms to determine when to use broad domain knowledge vs specific retrieved document context for clarification. We propose AsK, a novel hybrid approach that dynamically chooses between document-based or aspect-based clarification based on query ambiguity. Our approach requires no labeled clarification data and introduces: (1) Weakly-supervised Longformer-based ambiguity analysis, (2) Automated domain-specific aspect generation using clustering and LLMs and (3) LLM-powered clarification generation. AsK demonstrates significant improvements over baselines in both single-turn and multi-turn settings (recall@5 gain of ~20%) when evaluated on product troubleshooting and product search datasets.
LEAP & LEAN: Look-ahead Planning and Agile Navigation for LLM Agents
Authors: Nikhil Verma; Manasa Bharadwaj
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track))

Abstract: Foundational models endowed with emergent abilities are increasingly deployed as autonomous agents to navigate intricate environments. Despite their capability to comprehend human intentions, even when paired with reasoning traces, they struggle to achieve robust autonomy. In this work, we introduce **LEAP & LEAN**, a novel paradigm designed to enhance the performance of Large Language Models (LLMs) as autonomous agents. LEAP employs look-ahead planning to refine action selection, while LEAN streamlines navigation through agile prompt construction, enabling more efficient task completion. Together, LEAP & LEAN address the explore-exploit dilemma, fostering optimal decision-making and improving task performance. We evaluate our framework across diverse, multi-faceted task-oriented domains (WebShop, ALFWorld, and TravelPlanner) using both proprietary and open-source LLM agents. Notably, without any fine-tuning, our framework outperforms agents trained via imitation learning, reinforcement learning, and reasoning-based approaches. Our findings underscore the importance of action and prompt curation to create robust and efficient fully autonomous LLM agents.
MotiR: Motivation-aware Retrieval for Long-Tail Recommendation
Authors: Kaichen Zhao; Mingming Li; Haiquan Zhao; Kuien Liu; Zhixu Li; Xueying Li
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track))

Abstract: In the retrieval stage of recommendation systems, two-tower models are widely adopted for their efficiency as a predominant paradigm. However, this method, which relies on collaborative filtering signals, exhibits limitations in modeling similarity for long-tail items. To address this issue, we propose a Motivation-aware Retrieval for Long-Tail Recommendation, named MotiR. The purchase motivations generated by LLMs represent a condensed abstraction of items’ intrinsic attributes. By effectively integrating them with traditional item features, this approach enables the two-tower model to capture semantic-level similarities among long-tail items. Furthermore, a gated network-based adaptive weighting mechanism dynamically adjusts representation weights: emphasizing semantic modeling for long-tail items while preserving collaborative signal advantages for popular items. Experimental results demonstrate 60.5% Hit@10 improvements over existing methods on Amazon Books. Industrial deployment in Taobao&Tmall Group 88VIP scenarios achieves over 4% CTR and CVR improvement, validating the effectiveness of our method.
A Framework for Flexible Extraction of Clinical Event Contextual Properties from Electronic Health Records
Authors: Shubham Agarwal; Thomas Searle; Mart Ratas; Anthony Shek; James Teo; Richard Dobson
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track))

Abstract: Electronic Health Records contain vast amounts of valuable clinical data, much of which is stored as unstructured text. Extracting meaningful clinical events (e.g., disorders, symptoms, findings, medications, and procedures etc.) in context within real-world healthcare settings is crucial for enabling downstream applications such as disease prediction, clinical coding for billing and decision support.After Named Entity Recognition and Linking (NER+L) methodology, the identified concepts need to be further classified (i.e. contextualized) for distinct properties such as their relevance to the patient, their temporal and negated status for meaningful clinical use. We present a solution that, using an existing NER+L approach - MedCAT, classifies and contextualizes medical entities at scale. We evaluate the NLP approaches through 14 distinct real-world clinical text classification projects, testing our suite of models tailored to different clinical NLP needs. For tasks requiring high minority class recall, BERT proves the most effective when coupled with class imbalance mitigation techniques, outperforming Bi-LSTM with up to 28%. For majority class focused tasks, Bi-LSTM offers a lightweight alternative with, on average, 32% faster training time and lower computational cost. Importantly, these tools are integrated into an openly available library, enabling users to select the best model for their specific downstream applications.