English
Related papers

Related papers: Self-Refinement Strategies for LLM-based Product A…

200 papers

Federated search, which involves integrating results from multiple independent search engines, will become increasingly pivotal in the context of Retrieval-Augmented Generation pipelines empowering LLM-based applications such as chatbots.…

Information Retrieval · Computer Science 2024-02-01 Shuai Wang , Shengyao Zhuang , Bevan Koopman , Guido Zuccon

Recent large language models (LLM) are leveraging human feedback to improve their generation quality. However, human feedback is costly to obtain, especially during inference. In this work, we propose LLMRefine, an inference time…

Computation and Language · Computer Science 2024-10-28 Wenda Xu , Daniel Deutsch , Mara Finkelstein , Juraj Juraska , Biao Zhang , Zhongtao Liu , William Yang Wang , Lei Li , Markus Freitag

Harnessing the power of human-annotated data through Supervised Fine-Tuning (SFT) is pivotal for advancing Large Language Models (LLMs). In this paper, we delve into the prospect of growing a strong LLM out of a weak one without the need…

Machine Learning · Computer Science 2024-06-18 Zixiang Chen , Yihe Deng , Huizhuo Yuan , Kaixuan Ji , Quanquan Gu

New technologies in generative AI can enable deeper analysis into our nation's supply chains but truly informative insights require the continual updating and aggregation of massive data in a timely manner. Large Language Models (LLMs)…

Code review is essential for maintaining software quality but often time-consuming and cognitively demanding, especially in industrial environments. Recent advancements in language models (LMs) have opened new avenues for automating core…

Software Engineering · Computer Science 2025-10-24 Igli Begolli , Meltem Aksoy , Daniel Neider

Automated assessment in natural language generation is a challenging task. Instruction-tuned large language models (LLMs) have shown promise in reference-free evaluation, particularly through comparative assessment. However, the quadratic…

Computation and Language · Computer Science 2024-09-25 Vatsal Raina , Adian Liusie , Mark Gales

With the advent of large language models (LLMs), the vast unstructured text within millions of academic papers is increasingly accessible for materials discovery, although significant challenges remain. While LLMs offer promising few- and…

Computation and Language · Computer Science 2025-09-30 Amit K Verma , Zhisong Zhang , Junwon Seo , Robin Kuo , Runbo Jiang , Emma Strubell , Anthony D Rollett

Efficient data exploration is crucial as data becomes increasingly important for accelerating processes, improving forecasts and developing new business models. Data consumers often spend 25-98 % of their time searching for suitable data…

Information Retrieval · Computer Science 2025-07-11 Lennart Busch , Daniel Tebernum , Gissel Velarde

Within the realm of software engineering, specialized tasks on code, such as program repair, present unique challenges, necessitating fine-tuning Large language models~(LLMs) to unlock state-of-the-art performance. Fine-tuning approaches…

Software Engineering · Computer Science 2025-09-23 Boyang Yang , Haoye Tian , Jiadong Ren , Hongyu Zhang , Jacques Klein , Tegawendé F. Bissyandé , Claire Le Goues , Shunfu Jin

The alignment of large language models (LLMs) is crucial not only for unlocking their potential in specific tasks but also for ensuring that responses meet human expectations and adhere to safety and ethical principles. Current alignment…

Computation and Language · Computer Science 2024-06-18 Ruijun Chen , Jiehao Liang , Shiping Gao , Fanqi Wan , Xiaojun Quan

This paper presents a novel approach to e-commerce payment fraud detection by integrating reinforcement learning (RL) with Large Language Models (LLMs). By framing transaction risk as a multi-step Markov Decision Process (MDP), RL optimizes…

Machine Learning · Computer Science 2025-09-24 Bo Qu , Zhurong Wang , Daisuke Yagi , Zhen Xu , Yang Zhao , Yinan Shan , Frank Zahradnik

We propose a refined approach to efficiently fine-tune large language models (LLMs) on specific domains like the mathematical domain by employing a budgeted subset selection method. Our approach combines utility and diversity metrics to…

Machine Learning · Computer Science 2025-05-06 Madhav Kotecha , Vijendra Kumar Vaishya , Smita Gautam , Suraj Racha

There are two common ways in which developers are incorporating proprietary and domain-specific data when building applications of Large Language Models (LLMs): Retrieval-Augmented Generation (RAG) and Fine-Tuning. RAG augments the prompt…

Large language models (LLMs) have significantly advanced Natural Language Processing (NLP) tasks in recent years. However, their universal nature poses limitations in scenarios requiring personalized responses, such as recommendation…

Computation and Language · Computer Science 2024-11-08 Stanisław Woźniak , Bartłomiej Koptyra , Arkadiusz Janz , Przemysław Kazienko , Jan Kocoń

Existing large language models (LLMs) driven search agents typically rely on prompt engineering to decouple the user queries into search plans, limiting their effectiveness in complex scenarios requiring reasoning. Furthermore, they suffer…

Information Retrieval · Computer Science 2025-05-27 Jinzheng Li , Sibo Ju , Yanzhou Su , Hongguang Li , Yiqing Shen

Entity search, i.e., finding the most similar entities to a query entity, faces unique challenges in e-commerce, where product similarity varies across categories and contexts. Traditional embedding-based approaches often struggle to…

Information Retrieval · Computer Science 2026-05-01 Yilun Zhu , Nikhita Vedula , Shervin Malmasi

Successful Artificial Intelligence systems often require numerous labeled data to extract information from document images. In this paper, we investigate the problem of improving the performance of Artificial Intelligence systems in…

Information Retrieval · Computer Science 2022-09-27 Bao-Sinh Nguyen , Dung Tien Le , Hieu M. Vu , Tuan Anh D. Nguyen , Minh-Tien Nguyen , Hung Le

Large language models (LLMs) often struggle to provide up-to-date information due to their one-time training and the constantly evolving nature of the world. To keep LLMs current, existing approaches typically involve continued pre-training…

Computation and Language · Computer Science 2025-05-19 Xiaoying Zhang , Baolin Peng , Ye Tian , Jingyan Zhou , Yipeng Zhang , Haitao Mi , Helen Meng

Language models (LMs) have demonstrated remarkable capabilities in NLP, yet adapting them efficiently and robustly to specific tasks remains challenging. As their scale and complexity grow, fine-tuning LMs on labelled data often…

Computation and Language · Computer Science 2025-06-27 Zhengyan Shi

A Comparison of Independent and Joint Fine-tuning Strategies for Retrieval-Augmented Generation Download PDF Neal Gregory Lawton, Alfy Samuel, Anoop Kumar, Daben Liu Published: 20 Aug 2025, Retrieval augmented generation (RAG) is a popular…

Computation and Language · Computer Science 2025-10-21 Neal Gregory Lawton , Alfy Samuel , Anoop Kumar , Daben Liu