14 citations · 20 across the 7 of their papers we have counts for
7 papers
VeriAgent: A Tool-Integrated Multi-Agent System with Evolving Memory for PPA-Aware RTL Code Generation
Yaoxiang Wang, Qi Shi, ShangZhan Li +6
LLMs have recently demonstrated strong capabilities in automatic RTL code generation, achieving high syntactic and functional correctness. However, most methods focus on functional…
Tool Graph Retriever: Exploring Dependency Graph-based Tool Retrieval for Large Language Models
Linfeng Gao, Yaoxiang Wang, Minlong Peng +4
With the remarkable advancement of AI agents, the number of their equipped tools is increasing rapidly. However, integrating all tool information into the limited model context bec…
EpiCoder: Encompassing Diversity and Complexity in Code Generation
Yaoxiang Wang, Haoling Li, Xin Zhang +10
Existing methods for code generation use code snippets as seed data, restricting the complexity and diversity of the synthesized data. In this paper, we introduce a novel feature t…
TDAG: A Multi-Agent Framework based on Dynamic Task Decomposition and Agent Generation
Yaoxiang Wang, Zhiyong Wu, Junfeng Yao +1
The emergence of Large Language Models (LLMs) like ChatGPT has inspired the development of LLM-based agents capable of addressing complex, real-world tasks. However, these agents o…
IBADR: an Iterative Bias-Aware Dataset Refinement Framework for Debiasing NLU models
Xiaoyue Wang, Xin Liu, Lijie Wang +3
As commonly-used methods for debiasing natural language understanding (NLU) models, dataset refinement approaches heavily rely on manual data analysis, and thus maybe unable to cov…
OpenICL: An Open-Source Framework for In-context Learning
Zhenyu Wu, YaoXiang Wang, Jiacheng Ye +4
In recent years, In-context Learning (ICL) has gained increasing attention and emerged as the new paradigm for large language model (LLM) evaluation. Unlike traditional fine-tuning…
Self-Adaptive In-Context Learning: An Information Compression Perspective for In-Context Example Selection and Ordering
Zhiyong Wu, Yaoxiang Wang, Jiacheng Ye +1
Despite the surprising few-shot performance of in-context learning (ICL), it is still a common practice to randomly sample examples to serve as context. This paper advocates a new…