Zobrazeno 1 - 10
of 6 322
pro vyhledávání: '"Query Rewriting"'
Autor:
Li, Zhicong, Wang, Jiahao, Jiang, Zhishu, Mao, Hangyu, Chen, Zhongxia, Du, Jiazhen, Zhang, Yuanxing, Zhang, Fuzheng, Zhang, Di, Liu, Yong
Large language models often encounter challenges with static knowledge and hallucinations, which undermine their reliability. Retrieval-augmented generation (RAG) mitigates these issues by incorporating external information. However, user queries fre
Externí odkaz:
http://arxiv.org/abs/2411.13154
Analyzing unstructured data has been a persistent challenge in data processing. Large Language Models (LLMs) have shown promise in this regard, leading to recent proposals for declarative frameworks for LLM-powered processing of unstructured data. Ho
Externí odkaz:
http://arxiv.org/abs/2410.12189
In a real-world RAG system, the current query often involves spoken ellipses and ambiguous references from dialogue contexts, necessitating query rewriting to better describe user's information needs. However, traditional context-based rewriting has
Externí odkaz:
http://arxiv.org/abs/2408.17072
Query rewriting aims to generate a new query that can complement the original query to improve the information retrieval system. Recent studies on query rewriting, such as query2doc, query2expand and querey2cot, rely on the internal knowledge of Larg
Externí odkaz:
http://arxiv.org/abs/2407.12529
Query rewriting is a crucial technique for passage retrieval in open-domain conversational question answering (CQA). It decontexualizes conversational queries into self-contained questions suitable for off-the-shelf retrievers. Existing methods attem
Externí odkaz:
http://arxiv.org/abs/2406.10991
Autor:
Mo, Fengran, Ghaddar, Abbas, Mao, Kelong, Rezagholizadeh, Mehdi, Chen, Boxing, Liu, Qun, Nie, Jian-Yun
In this paper, we study how open-source large language models (LLMs) can be effectively deployed for improving query rewriting in conversational search, especially for ambiguous queries. We introduce CHIQ, a two-step method that leverages the capabil
Externí odkaz:
http://arxiv.org/abs/2406.05013
Autor:
Mao, Shengyu, Jiang, Yong, Chen, Boli, Li, Xiao, Wang, Peng, Wang, Xinyu, Xie, Pengjun, Huang, Fei, Chen, Huajun, Zhang, Ningyu
As Large Language Models (LLMs) and Retrieval Augmentation Generation (RAG) techniques have evolved, query rewriting has been widely incorporated into the RAG system for downstream tasks like open-domain QA. Many works have attempted to utilize small
Externí odkaz:
http://arxiv.org/abs/2405.14431
Autor:
Kostric, Ivica, Balog, Krisztian
Conversational passage retrieval is challenging as it often requires the resolution of references to previous utterances and needs to deal with the complexities of natural language, such as coreference and ellipsis. To address these challenges, pre-t
Externí odkaz:
http://arxiv.org/abs/2406.18960
Image search stands as a pivotal task in multimedia and computer vision, finding applications across diverse domains, ranging from internet search to medical diagnostics. Conventional image search systems operate by accepting textual or visual querie
Externí odkaz:
http://arxiv.org/abs/2404.18746
Autor:
Liu, Jie, Mozafari, Barzan
Query rewriting is one of the most effective techniques for coping with poorly written queries before passing them down to the query optimizer. Manual rewriting is not scalable, as it is error-prone and requires deep expertise. Similarly, traditional
Externí odkaz:
http://arxiv.org/abs/2403.09060