4 papers
How Large Language Models Balance Internal Knowledge with User and Document Assertions
Shuowei Li, Haoxin Li, Wenda Chu +1
Large language models (LLMs) often need to balance their internal parametric knowledge with external information, such as user beliefs and content from retrieved documents, in real…
Large language model as user daily behavior data generator: balancing population diversity and individual personality
Haoxin Li, Jingtao Ding, Jiahui Gong +1
Predicting human daily behavior is challenging due to the complexity of routine patterns and short-term fluctuations. While data-driven models have improved behavior prediction by…
Summarization-Based Document IDs for Generative Retrieval with Language Models
Haoxin Li, Daniel Cheng, Phillip Keung +2
Generative retrieval (Wang et al., 2022; Tay et al., 2022) is a popular approach for end-to-end document retrieval that directly generates document identifiers given an input query…
NarrowBERT: Accelerating Masked Language Model Pretraining and Inference
Haoxin Li, Phillip Keung, Daniel Cheng +2
Large-scale language model pretraining is a very successful form of self-supervised learning in natural language processing, but it is increasingly expensive to perform as the mode…