9 citations · 9 across the 4 of their papers we have counts for
4 papers
An Empirical Study of Interaction Smells in Multi-Turn Human-LLM Collaborative Code Generation
Binquan Zhang, Li Zhang, Lin Shi +6
Large Language Models (LLMs) have revolutionized code generation, evolving from static tools into dynamic conversational interfaces that facilitate complex, multi-turn collaborativ…
Decoding Human-LLM Collaboration in Coding: An Empirical Study of Multi-Turn Conversations in the Wild
Binquan Zhang, Li Zhang, Haoyuan Zhang +5
Large language models (LLMs) are increasingly acting as dynamic conversational interfaces, supporting multi-turn interactions that mimic human-like conversation and facilitate comp…
Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation
Binquan Zhang, Li Zhang, Zhiwen Luo +4
Large language models (LLMs) have demonstrated impressive performance in code generation, particularly when augmented with chain-of-thought (CoT) prompting techniques. They break d…
ClarifyGPT: Empowering LLM-based Code Generation with Intention Clarification
Fangwen Mu, Lin Shi, Song Wang +5
We introduce a novel framework named ClarifyGPT, which aims to enhance code generation by empowering LLMs with the ability to identify ambiguous requirements and ask targeted clari…