4 papers · 1 filter
PretrainRL: Alleviating Factuality Hallucination of Large Language Models at the Beginning
Langming Liu, Kangtao Lv, Haibin Chen +8
Large language models (LLMs), despite their powerful capabilities, suffer from factual hallucinations where they generate verifiable falsehoods. We identify a root of this issue: t…
Data Distribution Matters: A Data-Centric Perspective on Context Compression for Large Language Model
Kangtao Lv, Jiwei Tang, Langming Liu +7
The deployment of Large Language Models (LLMs) in long-context scenarios is hindered by computational inefficiency and significant information redundancy. Although recent advanceme…
How to inject knowledge efficiently? Knowledge Infusion Scaling Law for Pre-training Large Language Models
Kangtao Lv, Haibin Chen, Yujin Yuan +5
Large language models (LLMs) have attracted significant attention due to their impressive general capabilities across diverse downstream tasks. However, without domain-specific opt…
Training-free LLM-generated Text Detection by Mining Token Probability Sequences
Yihuai Xu, Yongwei Wang, Yifei Bi +4
Large language models (LLMs) have demonstrated remarkable capabilities in generating high-quality texts across diverse domains. However, the potential misuse of LLMs has raised sig…