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 are effective text rankers with pairwise ranking prompting
Ranking documents using Large Language Models (LLMs) by directly feeding the query and
candidate documents into the prompt is an interesting and practical problem. However …
candidate documents into the prompt is an interesting and practical problem. However …
Generative relevance feedback with large language models
Current query expansion models use pseudo-relevance feedback to improve first-pass
retrieval effectiveness; however, this fails when the initial results are not relevant. Instead of …
retrieval effectiveness; however, this fails when the initial results are not relevant. Instead of …
A survey on knowledge distillation of large language models
This survey presents an in-depth exploration of knowledge distillation (KD) techniques
within the realm of Large Language Models (LLMs), spotlighting the pivotal role of KD in …
within the realm of Large Language Models (LLMs), spotlighting the pivotal role of KD in …
Generative multi-modal knowledge retrieval with large language models
Knowledge retrieval with multi-modal queries plays a crucial role in supporting knowledge-
intensive multi-modal applications. However, existing methods face challenges in terms of …
intensive multi-modal applications. However, existing methods face challenges in terms of …
Perspectives on large language models for relevance judgment
When asked, large language models~(LLMs) like ChatGPT claim that they can assist with
relevance judgments but it is not clear whether automated judgments can reliably be used in …
relevance judgments but it is not clear whether automated judgments can reliably be used in …
TRAD: Enhancing LLM Agents with Step-Wise Thought Retrieval and Aligned Decision
Several large language model (LLM) agents have been constructed for diverse purposes
such as web navigation and online shopping, leveraging the broad knowledge and text …
such as web navigation and online shopping, leveraging the broad knowledge and text …
Generative and pseudo-relevant feedback for sparse, dense and learned sparse retrieval
Pseudo-relevance feedback (PRF) is a classical approach to address lexical mismatch by
enriching the query using first-pass retrieval. Moreover, recent work on generative-relevance …
enriching the query using first-pass retrieval. Moreover, recent work on generative-relevance …
Leveraging LLM Reasoning Enhances Personalized Recommender Systems
Recent advancements have showcased the potential of Large Language Models (LLMs) in
executing reasoning tasks, particularly facilitated by Chain-of-Thought (CoT) prompting …
executing reasoning tasks, particularly facilitated by Chain-of-Thought (CoT) prompting …
Human vs ChatGPT: Effect of Data Annotation in Interpretable Crisis-Related Microblog Classification
Recent studies have exploited the vital role of microblogging platforms, such as Twitter, in
crisis situations. Various machine-learning approaches have been proposed to identify and …
crisis situations. Various machine-learning approaches have been proposed to identify and …