7 citations · 30 across the 11 of their papers we have counts for
11 papers
DocReward: A Document Reward Model for Structuring and Stylizing
Junpeng Liu, Yuzhong Zhao, Bowen Cao +17
Recent agentic workflows automate professional document generation but focus narrowly on textual quality, overlooking structural and stylistic professionalism, which is equally cri…
WildLong: Synthesizing Realistic Long-Context Instruction Data at Scale
Jiaxi Li, Xingxing Zhang, Xun Wang +6
Large language models (LLMs) with extended context windows enable tasks requiring extensive information integration but are limited by the scarcity of high-quality, diverse dataset…
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…
ALYMPICS: LLM Agents Meet Game Theory -- Exploring Strategic Decision-Making with AI Agents
Shaoguang Mao, Yuzhe Cai, Yan Xia +5
This paper introduces Alympics (Olympics for Agents), a systematic simulation framework utilizing Large Language Model (LLM) agents for game theory research. Alympics creates a ver…
SCALE: Synergized Collaboration of Asymmetric Language Translation Engines
Xin Cheng, Xun Wang, Tao Ge +4
In this paper, we introduce SCALE, a collaborative framework that connects compact Specialized Translation Models (STMs) and general-purpose Large Language Models (LLMs) as one uni…
In-context Autoencoder for Context Compression in a Large Language Model
Tao Ge, Jing Hu, Lei Wang +3
We propose the In-context Autoencoder (ICAE), leveraging the power of a large language model (LLM) to compress a long context into short compact memory slots that can be directly c…
Smart Word Suggestions for Writing Assistance
Chenshuo Wang, Shaoguang Mao, Tao Ge +5
Enhancing word usage is a desired feature for writing assistance. To further advance research in this area, this paper introduces "Smart Word Suggestions" (SWS) task and benchmark.…
Extensible Prompts for Language Models on Zero-shot Language Style Customization
Tao Ge, Jing Hu, Li Dong +5
We propose eXtensible Prompt (X-Prompt) for prompting a large language model (LLM) beyond natural language (NL). X-Prompt instructs an LLM with not only NL but also an extensible v…