59 citations · 59 across the 4 of their papers we have counts for
4 papers
Retrieval as Generation: A Unified Framework with Self-Triggered Information Planning
Bo Li, Mingda Wang, Gexiang Fang +2
We revisit retrieval-augmented generation (RAG) by embedding retrieval control directly into generation. Instead of treating retrieval as an external intervention, we express retri…
An Empirical Study on Influence-Based Pretraining Data Selection for Code Large Language Models
Chengli Xing, Zhengran Zeng, Gexiang Fang +3
Recent advancements in code large language models (Code-LLMs) have demonstrated remarkable capabilities in resolving programming related tasks. Meanwhile, researchers have recogniz…
MPL: Multiple Programming Languages with Large Language Models for Information Extraction
Bo Li, Gexiang Fang, Wei Ye +4
Recent research in information extraction (IE) focuses on utilizing code-style inputs to enhance structured output generation. The intuition behind this is that the programming lan…
Evaluating ChatGPT's Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulness
Bo Li, Gexiang Fang, Yang Yang +4
The capability of Large Language Models (LLMs) like ChatGPT to comprehend user intent and provide reasonable responses has made them extremely popular lately. In this paper, we foc…