Large language models for information retrieval: A survey
As a primary means of information acquisition, information retrieval (IR) systems, such as
search engines, have integrated themselves into our daily lives. These systems also serve …
search engines, have integrated themselves into our daily lives. These systems also serve …
Large language models know your contextual search intent: A prompting framework for conversational search
Precisely understanding users' contextual search intent has been an important challenge for
conversational search. As conversational search sessions are much more diverse and long …
conversational search. As conversational search sessions are much more diverse and long …
Re-reading improves reasoning in language models
Reasoning presents a significant and challenging issue for Large Language Models (LLMs).
The predominant focus of research has revolved around developing diverse prompting …
The predominant focus of research has revolved around developing diverse prompting …
Llatrieval: Llm-verified retrieval for verifiable generation
Verifiable generation aims to let the large language model (LLM) generate text with
corresponding supporting documents, which enables the user to flexibly verify the answer …
corresponding supporting documents, which enables the user to flexibly verify the answer …
Instructor: Instructing unsupervised conversational dense retrieval with large language models
Compared to traditional single-turn ad-hoc retrieval, conversational retrieval needs to
handle the multi-turn conversation and understand the user's real query intent. However …
handle the multi-turn conversation and understand the user's real query intent. However …
Unims-rag: A unified multi-source retrieval-augmented generation for personalized dialogue systems
Large Language Models (LLMs) has shown exceptional capabilities in many natual
language understanding and generation tasks. However, the personalization issue still …
language understanding and generation tasks. However, the personalization issue still …
ICXML: An in-context learning framework for zero-shot extreme multi-label classification
This paper focuses on the task of Extreme Multi-Label Classification (XMC) whose goal is to
predict multiple labels for each instance from an extremely large label space. While existing …
predict multiple labels for each instance from an extremely large label space. While existing …
Steering Large Language Models for Cross-lingual Information Retrieval
In today's digital age, accessing information across language barriers poses a significant
challenge, with conventional search systems often struggling to interpret and retrieve …
challenge, with conventional search systems often struggling to interpret and retrieve …
Cognitive personalized search integrating large language models with an efficient memory mechanism
Traditional search engines usually provide identical search results for all users, overlooking
individual preferences. To counter this limitation, personalized search has been developed …
individual preferences. To counter this limitation, personalized search has been developed …
Agent4ranking: Semantic robust ranking via personalized query rewriting using multi-agent llm
Search engines are crucial as they provide an efficient and easy way to access vast
amounts of information on the internet for diverse information needs. User queries, even with …
amounts of information on the internet for diverse information needs. User queries, even with …