Human evaluation provides a direct measure of the quality of LLM-generated web applications. However, fitting human judgments through automated evaluation remains challenging. Static benchmarks can credit functionality that exists in source code but is unreachable at runtime. Interactive benchmarks exercise the applica...
Chen-Xu Liu, Zi-Lu Zou, Pei-Zhong Gao et al.· 0 citations
Agent benchmarks are substantially more costly to evaluate than conventional LLM benchmarks. Benchmark compression is therefore a natural solution, yet existing methods primarily model redundancy in task--model final-score distributions, which is important in agentic evaluation. To address this limitation, we analyze l...
Xin-Shuai Guo, Jun-Jie Wu, Dolly Deng et al.· 0 citations
Human evaluation provides a direct measure of the quality of LLM-generated web applications. However, fitting human judgments through automated evaluation remains challenging. Static benchmarks can credit functionality that exists in source code but is unreachable at runtime. Interactive benchmarks exercise the applica...
Chen-Xu Liu, Zi-Lu Zou, Pei-Zhong Gao et al.· 0 citations
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