1 citations · 1 across the 7 of their papers we have counts for
8 papers
U-Fold: Dynamic Intent-Aware Context Folding for User-Centric Agents
Jin Su, Runnan Fang, Yeqiu Li +5
Large language model (LLM)-based agents have been successfully deployed in many tool-augmented settings, but their scalability is fundamentally constrained by context length. Exist…
Each Rank Could be an Expert: Single-Ranked Mixture of Experts LoRA for Multi-Task Learning
Ziyu Zhao, Yixiao Zhou, Zhi Zhang +10
Low-Rank Adaptation (LoRA) is widely used for adapting large language models (LLMs) to specific domains due to its efficiency and modularity. Meanwhile, vanilla LoRA struggles with…
Enhancing Adversarial Attacks through Chain of Thought
Jingbo Su
Large language models (LLMs) have demonstrated impressive performance across various domains but remain susceptible to safety concerns. Prior research indicates that gradient-based…
Merging LoRAs like Playing LEGO: Pushing the Modularity of LoRA to Extremes Through Rank-Wise Clustering
Ziyu Zhao, Tao Shen, Didi Zhu +5
Low-Rank Adaptation (LoRA) has emerged as a popular technique for fine-tuning large language models (LLMs) to various domains due to its modular design and widespread availability…
BabelBench: An Omni Benchmark for Code-Driven Analysis of Multimodal and Multistructured Data
Xuwu Wang, Qiwen Cui, Yunzhe Tao +16
Large language models (LLMs) have become increasingly pivotal across various domains, especially in handling complex data types. This includes structured data processing, as exempl…
An Expert is Worth One Token: Synergizing Multiple Expert LLMs as Generalist via Expert Token Routing
Ziwei Chai, Guoyin Wang, Jing Su +8
We present Expert-Token-Routing, a unified generalist framework that facilitates seamless integration of multiple expert LLMs. Our framework represents expert LLMs as special exper…
Empowering Large Language Model Agents through Action Learning
Haiteng Zhao, Chang Ma, Guoyin Wang +5
Large Language Model (LLM) Agents have recently garnered increasing interest yet they are limited in their ability to learn from trial and error, a key element of intelligent behav…
InfiAgent-DABench: Evaluating Agents on Data Analysis Tasks
Xueyu Hu, Ziyu Zhao, Shuang Wei +14
In this paper, we introduce InfiAgent-DABench, the first benchmark specifically designed to evaluate LLM-based agents on data analysis tasks. These tasks require agents to end-to-e…