20 citations · 57 across the 11 of their papers we have counts for
11 papers
EvoArena: Tracking Memory Evolution for Robust LLM Agents in Dynamic Environments
Jundong Xu, Qingchuan Li, Jiaying Wu +11
Large language model (LLM) agents have achieved strong performance on a wide range of benchmarks, yet most evaluations assume static environments. In contrast, real-world deploymen…
From Perception to Action: An Interactive Benchmark for Vision Reasoning
Yuhao Wu, Maojia Song, Yihuai Lan +8
Understanding the physical structure is essential for real-world applications such as embodied agents, interactive design, and long-horizon manipulation. Yet, prevailing Vision-Lan…
FedReFT: Federated Representation Fine-Tuning with All-But-Me Aggregation
Fatema Siddika, Md Anwar Hossen, J. Pablo Muñoz +3
Parameter-efficient fine-tuning (PEFT) adapts large pre-trained models by updating only a small subset of parameters. Recently, Representation Fine-Tuning (ReFT) has emerged as an…
An Empirical Study on Prompt Compression for Large Language Models
Zheng Zhang, Jinyi Li, Yihuai Lan +2
Prompt engineering enables Large Language Models (LLMs) to perform a variety of tasks. However, lengthy prompts significantly increase computational complexity and economic costs.…
DVM: Towards Controllable LLM Agents in Social Deduction Games
Zheng Zhang, Yihuai Lan, Yangsen Chen +3
Large Language Models (LLMs) have advanced the capability of game agents in social deduction games (SDGs). These games rely heavily on conversation-driven interactions and require…
All in an Aggregated Image for In-Image Learning
Lei Wang, Wanyu Xu, Zhiqiang Hu +5
This paper introduces a new in-context learning (ICL) mechanism called In-Image Learning (IL) that combines demonstration examples, visual cues, and chain-of-thought reasoning…
PCToolkit: A Unified Plug-and-Play Prompt Compression Toolkit of Large Language Models
Jinyi Li, Yihuai Lan, Lei Wang +1
Prompt compression is an innovative method for efficiently condensing input prompts while preserving essential information. To facilitate quick-start services, user-friendly interf…
LLM-Based Agent Society Investigation: Collaboration and Confrontation in Avalon Gameplay
Yihuai Lan, Zhiqiang Hu, Lei Wang +6
This paper explores the open research problem of understanding the social behaviors of LLM-based agents. Using Avalon as a testbed, we employ system prompts to guide LLM agents in…
Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models
Lei Wang, Wanyu Xu, Yihuai Lan +4
Large language models (LLMs) have recently been shown to deliver impressive performance in various NLP tasks. To tackle multi-step reasoning tasks, few-shot chain-of-thought (CoT)…
LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models
Zhiqiang Hu, Lei Wang, Yihuai Lan +6
The success of large language models (LLMs), like GPT-4 and ChatGPT, has led to the development of numerous cost-effective and accessible alternatives that are created by finetunin…
MWPToolkit: An Open-Source Framework for Deep Learning-Based Math Word Problem Solvers
Yihuai Lan, Lei Wang, Qiyuan Zhang +5
Developing automatic Math Word Problem (MWP) solvers has been an interest of NLP researchers since the 1960s. Over the last few years, there are a growing number of datasets and de…