1 citations · 1 across the 2 of their papers we have counts for
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Compressing Code Context for LLM-based Issue Resolution
Haoxiang Jia, Earl T. Barr, Sergey Mechtaev
Large Language Models (LLMs) are now capable of resolving real-world GitHub issues. However, current approaches overapproximate the code context and suffer from two compounding pro…
Statistical Independence Aware Caching for LLM Workflows
Yihan Dai, Dimitrios Stamatios Bouras, Haoxiang Jia +1
Large language models (LLMs) inference is both expensive and slow. Local caching of responses offers a practical solution to reduce the cost and latency of LLM queries. In research…
Automated Repair of Ambiguous Problem Descriptions for LLM-Based Code Generation
Haoxiang Jia, Robbie Morris, He Ye +2
The growing use of large language models (LLMs) has increased the importance of natural language (NL) in software engineering. However, ambiguity of NL can harm software quality, a…