1 citations · 1 across the 5 of their papers we have counts for
5 papers
ATACompressor: Adaptive Task-Aware Compression for Efficient Long-Context Processing in LLMs
Xuancheng Li, Haitao Li, Yujia Zhou +2
Long-context inputs in large language models (LLMs) often suffer from the "lost in the middle" problem, where critical information becomes diluted or ignored due to excessive lengt…
LegalOne: A Family of Foundation Models for Reliable Legal Reasoning
Haitao Li, Yifan Chen, Shuo Miao +13
While Large Language Models (LLMs) have demonstrated impressive general capabilities, their direct application in the legal domain is often hindered by a lack of precise domain kno…
From <Answer> to <Think>: Multidimensional Supervision of Reasoning Process for LLM Optimization
Beining Wang, Weihang Su, Hongtao Tian +5
Improving the multi-step reasoning ability of Large Language Models (LLMs) is a critical yet challenging task. The dominant paradigm, outcome-supervised reinforcement learning (RLV…
RbFT: Robust Fine-tuning for Retrieval-Augmented Generation against Retrieval Defects
Yiteng Tu, Weihang Su, Yujia Zhou +2
Retrieval-augmented generation (RAG) enhances large language models (LLMs) by integrating external knowledge retrieved from a knowledge base. However, its effectiveness is fundamen…
Parametric Retrieval Augmented Generation
Weihang Su, Yichen Tang, Qingyao Ai +6
Retrieval-augmented generation (RAG) techniques have emerged as a promising solution to enhance the reliability of large language models (LLMs) by addressing issues like hallucinat…