Search Results for author:
Found 4 papers, 3 papers with code
Generalizing Conversational Dense Retrieval via LLM-Cognition Data Augmentation
Conversational search utilizes muli-turn natural language contexts to retrieve relevant passages.
Large Language Models for Information Retrieval: A Survey
This evolution requires a combination of both traditional methods (such as term-based sparse retrieval methods with rapid response) and modern neural architectures (such as language models with powerful language understanding capacity).
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.
JDsearch: A Personalized Product Search Dataset with Real Queries and Full Interactions
To evaluate the effectiveness of these models, previous studies mainly utilize the simulated Amazon recommendation dataset, which contains automatically generated queries and excludes cold users and tail products.
