Search Results for author:
Found 5 papers, 3 papers with code
From Matching to Generation: A Survey on Generative Information Retrieval
We will summarize the advancements in GR regarding model training, document identifier, incremental learning, downstream tasks adaptation, multi-modal GR and generative recommendation, as well as progress in reliable response generation in aspects of internal knowledge memorization, external knowledge augmentation, generating response with citations and personal information assistant.
BIDER: Bridging Knowledge Inconsistency for Efficient Retrieval-Augmented LLMs via Key Supporting Evidence
Retrieval-augmented large language models (LLMs) have demonstrated efficacy in knowledge-intensive tasks such as open-domain QA, addressing inherent challenges in knowledge update and factual inadequacy.
Metacognitive Retrieval-Augmented Large Language Models
Drawing from cognitive psychology, metacognition allows an entity to self-reflect and critically evaluate its cognitive processes.
Cognitive Personalized Search Integrating Large Language Models with an Efficient Memory Mechanism
To counter this limitation, personalized search has been developed to re-rank results based on user preferences derived from query logs.
RETA-LLM: A Retrieval-Augmented Large Language Model Toolkit
To support research in this area and facilitate the development of retrieval-augmented LLM systems, we develop RETA-LLM, a {RET}reival-{A}ugmented LLM toolkit.
