Enterprise AI Analysis
Can LLMs Replace Human Evaluators? An Empirical Study of LLM-as-a-Judge in Software Engineering
In this paper, we empirically explore LLM-as-a-judge methods for evaluating SE tasks, focusing on their alignment with human judgments. We select seven LLM-as-a-judge methods that utilize general-purpose LLMs, alongside two LLMs specifically fine-tuned for evaluation. After generating and manually scoring LLM responses on three recent SE datasets of code translation, code generation, and code summarization, we then prompt these methods to evaluate each response. Finally, we compare the scores generated by these methods with human evaluation. The results indicate that output-based methods reach the highest Pearson correlation of 81.32 and 68.51 with human scores in code translation and generation, achieving near-human evaluation, noticeably outperforming ChrF++, one of the best conventional metrics, at 34.23 and 64.92. Such output-based methods prompt LLMs to output judgments directly, and exhibit more balanced score distributions that resemble human score patterns. Finally, we provide insights and implications, concluding that current state-of-the-art LLM-as-a-judge methods can potentially replace human evaluations in certain SE tasks.
Executive Impact & Key Findings
LLM-as-a-judge methods show promise for evaluating Software Engineering (SE) tasks, with output-based methods demonstrating near-human alignment in code translation and generation. They often outperform conventional metrics but struggle in code summarization. Consistency in pairwise comparisons remains a challenge, and the size and training of LLMs significantly impact performance.
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Pearson Correlation (Code Translation)
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Pearson Correlation (Code Generation)
Output-Based
Top Performing Method Category
Deep Analysis & Enterprise Applications
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This section details how different LLM-as-a-judge methods align with human preferences across various Software Engineering tasks. It explores the strengths and weaknesses of embedding-based, probability-based, and output-based approaches, highlighting their correlation coefficients (Spearman's ρ, Pearson's R, Kendall's τ) against human scores. Key findings include task-dependent performance and the superior alignment of large, output-based LLMs in specific contexts.
81.32%
Peak Human Alignment (Code Translation)
| Method Category | Code Translation (R) | Code Generation (R) | Code Summarization (R) |
|---|---|---|---|
| Conventional Metrics | 34.23 | 65.55 | 47.01 |
| Embedding-Based | 32.49 | 47.35 | 29.44 |
| Probability-Based | 34.77 | 45.42 | 29.62 |
| Output-Based (Large LLM) | 81.32 | 68.51 | 26.19 |
This tab investigates the score distributions and inter-method correlations of LLM-as-a-judge approaches. It examines whether methods within the same category produce similar scores and how these distributions compare to human evaluations. The analysis reveals that output-based methods using large LLMs not only align well with human scores but also exhibit more balanced and human-like score distributions.
Enterprise Process Flow
Instruction Collection
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Response Generation
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Manual Evaluation
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LLM Evaluation
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Correlation Analysis
90.64%
Highest Inter-Method Correlation (Translation, Large LLMs)
This section evaluates the performance of LLMs in making pairwise comparisons between two responses, rather than assigning individual scores. It assesses accuracy and agreement (consistency upon reversing response order) for output-based methods. The findings highlight the current limitations of LLM-as-a-judge methods in this setup, often yielding inconsistent results.
| Method | Accuracy (Trans.) | Agreement (Trans.) | Accuracy (Gen.) | Agreement (Gen.) |
|---|---|---|---|---|
| Random Guess | 33.33% | 33.33% | 33.33% | 33.33% |
| GPT-4o (Vanilla) | 57.33% | 13.33% | 49.33% | 13.33% |
| BatchEval (GPT-4o) | 65.33% | 21.33% | 52.67% | 24.00% |
| Llama2 (SFT) | 36.00% | 78.67% | 34.67% | 72.67% |
Case Study: Explanations & Biases
This case study examines specific instances of LLM-generated explanations for scores, revealing potential biases. For example, GPT-4o accurately identifies critical discrepancies in code translation but exhibits verbosity bias in code summarization, assigning perfect scores to overly detailed summaries.
Conclusion: LLMs can provide insightful explanations, but their biases (e.g., verbosity) can lead to misalignment with human judgment, especially in nuanced tasks like summarization.
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