7 citations · 30 across the 11 of their papers we have counts for
4 papers · 1 filter
xRAG: Extreme Context Compression for Retrieval-augmented Generation with One Token
Xin Cheng, Xun Wang, Xingxing Zhang +5
This paper introduces xRAG, an innovative context compression method tailored for retrieval-augmented generation. xRAG reinterprets document embeddings in dense retrieval--traditio…
LLM as a Mastermind: A Survey of Strategic Reasoning with Large Language Models
Yadong Zhang, Shaoguang Mao, Tao Ge +7
This paper presents a comprehensive survey of the current status and opportunities for Large Language Models (LLMs) in strategic reasoning, a sophisticated form of reasoning that n…
Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models
Haoran Li, Qingxiu Dong, Zhengyang Tang +17
We introduce Generalized Instruction Tuning (called GLAN), a general and scalable method for instruction tuning of Large Language Models (LLMs). Unlike prior work that relies on se…
K-Level Reasoning: Establishing Higher Order Beliefs in Large Language Models for Strategic Reasoning
Yadong Zhang, Shaoguang Mao, Tao Ge +4
Strategic reasoning is a complex yet essential capability for intelligent agents. It requires Large Language Model (LLM) agents to adapt their strategies dynamically in multi-agent…