RES-Q: Evaluating Code-Editing Large Language Model Systems at the Repository Scale

• URL: http://arxiv.org/abs/2406.16801v2
• Date: Tue, 25 Jun 2024 18:04:23 GMT
• Title: RES-Q: Evaluating Code-Editing Large Language Model Systems at the Repository Scale
• Authors: Beck LaBash, August Rosedale, Alex Reents, Lucas Negritto, Colin Wiel,
• Abstract summary: We develop RES-Q, a benchmark for evaluating Large Language Models (LLMs) We evaluate various state-of-the-art LLMs as language agents in a repository-editing system built on Qurrent OS.
• Score: 3.378738346115004
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The instruction-following ability of Large Language Models (LLMs) has cultivated a class of LLM-based systems capable of approaching complex tasks such as making edits to large code repositories. Due to the high sensitivity and unpredictability of LLM behavior in response to changes in prompting, robust evaluation tools are needed to drive future iteration of these systems. We propose RES-Q, a natural language instruction-based benchmark for evaluating $\textbf{R}$epository $\textbf{E}$diting $\textbf{S}$ystems, which consists of 100 handcrafted repository editing tasks derived from real GitHub commits. Given an edit instruction and a code repository, RES-Q evaluates an LLM system's ability to interpret the instruction, navigate the repository to gather relevant information, and construct an appropriate edit that satisfies the specified criteria. We argue that evaluating LLMs in this way addresses issues with traditional benchmarks and provides a more holistic assessment of a model's abilities. We evaluate various state-of-the-art LLMs as language agents in a repository-editing system built on Qurrent OS, our language agent development software. Despite their 1% pass@1 performance difference on HumanEval, we find Claude Sonnet 3.5 outperforms GPT-4o by 12% pass@1 on RES-Q, indicating RES-Q's capacity to differentiate model capability as traditional benchmarks approach saturation. We further investigate token efficiency, performance relationships with existing benchmarks, and interesting disparities between closed and open-source LLMs. Code and dataset are available at https://github.com/Qurrent-AI/RES-Q.

Related papers

• Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation [2.249533649156367]
  Legacy software systems, written in outdated languages like MUMPS and mainframe assembly, pose challenges in efficiency, maintenance, staffing, and security. This paper investigates the utilization of LLMs to generate documentation for legacy code using two datasets. We propose a prompting strategy for generating line-wise code comments and a rubric to evaluate their completeness, readability, usefulness, and hallucination.
  arXiv  Detail & Related papers  (2024-11-22T14:27:27Z)
• SysBench: Can Large Language Models Follow System Messages? [30.701602680394686]
  Large Language Models (LLMs) have become instrumental across various applications, with the customization of these models to specific scenarios becoming increasingly critical. Despite the recognized potential of system messages to optimize AI-driven solutions, there is a notable absence of a benchmark for evaluating how well LLMs follow system messages. We introduce SysBench, a benchmark that systematically analyzes system message following ability in terms of three limitations of existing LLMs.
  arXiv  Detail & Related papers  (2024-08-20T15:33:16Z)
• RepoQA: Evaluating Long Context Code Understanding [12.329233433333416]
  RepoQA is a benchmark to evaluate Large Language Models (LLMs) on long-context code understanding. RepoQA includes 500 code search tasks gathered from 50 popular repositories across 5 modern programming languages.
  arXiv  Detail & Related papers  (2024-06-10T05:15:30Z)
• RepEval: Effective Text Evaluation with LLM Representation [55.26340302485898]
  RepEval is a metric that leverages the projection of Large Language Models (LLMs) representations for evaluation. Our work underscores the richness of information regarding text quality embedded within LLM representations, offering insights for the development of new metrics.
  arXiv  Detail & Related papers  (2024-04-30T13:50:55Z)
• Class-Level Code Generation from Natural Language Using Iterative, Tool-Enhanced Reasoning over Repository [4.767858874370881]
  We introduce RepoClassBench, a benchmark designed to rigorously evaluate LLMs in generating class-level code within real-world repositories. RepoClassBench includes "Natural Language to Class generation" tasks across Java, Python & C# from a selection of repositories. We introduce Retrieve-Repotools-Reflect (RRR), a novel approach that equips LLMs with static analysis tools to iteratively navigate & reason about repository-level context.
  arXiv  Detail & Related papers  (2024-04-22T03:52:54Z)
• If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents [81.60906807941188]
  Large language models (LLMs) are trained on a combination of natural language and formal language (code) Code translates high-level goals into executable steps, featuring standard syntax, logical consistency, abstraction, and modularity.
  arXiv  Detail & Related papers  (2024-01-01T16:51:20Z)
• CLOMO: Counterfactual Logical Modification with Large Language Models [109.60793869938534]
  We introduce a novel task, Counterfactual Logical Modification (CLOMO), and a high-quality human-annotated benchmark. In this task, LLMs must adeptly alter a given argumentative text to uphold a predetermined logical relationship. We propose an innovative evaluation metric, the Self-Evaluation Score (SES), to directly evaluate the natural language output of LLMs.
  arXiv  Detail & Related papers  (2023-11-29T08:29:54Z)
• ML-Bench: Evaluating Large Language Models and Agents for Machine Learning Tasks on Repository-Level Code [76.84199699772903]
  ML-Bench is a benchmark rooted in real-world programming applications that leverage existing code repositories to perform tasks. To evaluate both Large Language Models (LLMs) and AI agents, two setups are employed: ML-LLM-Bench for assessing LLMs' text-to-code conversion within a predefined deployment environment, and ML-Agent-Bench for testing autonomous agents in an end-to-end task execution within a Linux sandbox environment.
  arXiv  Detail & Related papers  (2023-11-16T12:03:21Z)
• Benchmarking Generation and Evaluation Capabilities of Large Language Models for Instruction Controllable Summarization [132.25202059478065]
  We benchmark large language models (LLMs) on instruction controllable text summarization. Our study reveals that instruction controllable text summarization remains a challenging task for LLMs.
  arXiv  Detail & Related papers  (2023-11-15T18:25:26Z)
• Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes [15.214583657626697]
  EVAPORATE is a prototype system powered by large language models (LLMs) Code synthesis is cheap, but far less accurate than directly processing each document with the LLM. We propose an extended code implementation, EVAPORATE-CODE+, which achieves better quality than direct extraction.
  arXiv  Detail & Related papers  (2023-04-19T06:00:26Z)
• Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models [68.37431984231338]
  Large language models (LLMs) have shown impressive results across a variety of tasks while requiring little or no direct supervision. We believe the ability of an LLM to an attribute to the text that it generates is likely to be crucial for both system developers and users in this setting.
  arXiv  Detail & Related papers  (2022-12-15T18:45:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.