# Automatic Generation of Benchmarks and Reliable LLM Judgment for Code Tasks

arXiv: https://arxiv.org/abs/2410.21071 · HTML: https://mlsanity.com/paper/2410.21071/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2024.

## Built on by (most cited first, 7 of 7)

- [Large Language Models (LLMs) for Source Code Analysis: applications, models and datasets](https://mlsanity.com/paper/2503.17502/) (2025)
- [CML-Bench: A Framework for Evaluating and Enhancing LLM-Powered Movie Scripts Generation](https://mlsanity.com/paper/2510.06231/) (2025)
- [Silencer: From Discovery to Mitigation of Self-Bias in LLM-as-Benchmark-Generator](https://mlsanity.com/paper/2505.20738/) (2025)
- [When LLMs Benchmark Themselves: Deconstructing Self-Bias in Automated Evaluation](https://mlsanity.com/paper/2509.26600/) (2025)
- [Automating Benchmark Design](https://mlsanity.com/paper/2510.25039/) (2025)
- [Technique to Baseline QE Artefact Generation Aligned to Quality Metrics](https://mlsanity.com/paper/2511.15733/) (2025)
- [SciCustom: A Framework for Custom Evaluation of Scientific Capabilities in Large Language Models](https://mlsanity.com/paper/2605.19357/) (2026)

## Abstract (arXiv)

LLMs can be used in a variety of code related tasks such as translating from one programming language to another, implementing natural language requirements and code summarization. Artifacts generated by state of the art LLM technology are expected to be useful in the sense that a user will be able to use the LLM generated artifact after a small number of easy modifications. Quantifying this vague notion is challenging and it is thus hard to determine the quality of code related LLM solutions. We refer to evaluation of LLM solutions using LLM judgment as "LLM as a Judge", or LaaJ for short. In this work we introduce a methodology to generate and evaluate LaaJ implementations, utilizing an automatically generated benchmark. The purpose of the benchmark is two fold, namely, it is used both to develop and validate the LaaJs and to validate and test the LLM code related solution using the LaaJs. To that end, we developed an automated benchmark generation engine, which generates code in multiple programming languages for multiple code related tasks and which serves as the input for LaaJ evaluation. We utilize a graph representation, G, of the potential code related generations. The graph vertices are generated artifacts and edges represent possible generations, e.g., the generation of a Java program from its natural language requirements. Utilizing a chain of LLM agents and G we generate code related artifacts. Using cycles in G we formulate expectations on the generated artifacts. Taking advantage of these formulated expectations enables the development and testing of reliable LLM judgement for usefulness of the artifacts generated by the solution. Our approach enables the creation of high quality code task solutions.