2 citations · 4 across the 5 of their papers we have counts for
5 papers
Test vs Mutant: Adversarial LLM Agents for Robust Unit Test Generation
Pengyu Chang, Yixiong Fang, Silin Chen +3
Software testing is a critical, yet resource-intensive phase of the software development lifecycle. Over the years, various automated tools have been developed to aid in this proce…
Reasoning in Trees: Improving Retrieval-Augmented Generation for Multi-Hop Question Answering
Yuling Shi, Maolin Sun, Zijun Liu +4
Retrieval-Augmented Generation (RAG) has demonstrated significant effectiveness in enhancing large language models (LLMs) for complex multi-hop question answering (QA). For multi-h…
LastingBench: Defend Benchmarks Against Knowledge Leakage
Yixiong Fang, Tianran Sun, Yuling Shi +2
The increasing complexity of large language models (LLMs) raises concerns about their ability to "cheat" on standard Question Answering (QA) benchmarks by memorizing task-specific…
AttentionRAG: Attention-Guided Context Pruning in Retrieval-Augmented Generation
Yixiong Fang, Tianran Sun, Yuling Shi +1
While RAG demonstrates remarkable capabilities in LLM applications, its effectiveness is hindered by the ever-increasing length of retrieved contexts, which introduces information…
CodeCipher: Learning to Obfuscate Source Code Against LLMs
Yalan Lin, Chengcheng Wan, Yixiong Fang +1
While large code language models have made significant strides in AI-assisted coding tasks, there are growing concerns about privacy challenges. The user code is transparent to the…