Improving Reliability of LLM-Generated Code Summaries through Calibrated Confidence Scores

Conceitos Básicos

Providing a reliable confidence measure that indicates the likelihood of an LLM-generated code summary being sufficiently similar to a human-written summary.

Resumo

The paper introduces the problem of calibration for code summarizers, framing "correctness" of generated summaries in terms of being sufficiently similar to human-generated summaries.

The authors examine the calibration of several LLMs, including GPT-3.5-Turbo, CodeLlama-70b, and DeepSeek-Coder-33b, across different programming languages (Java and Python) and prompting techniques (Retrieval Augmented Few-Shot Learning and Automatic Semantic Augmentation of Prompt).

The authors find that the LLMs' own confidence measures (average token probability) are not well-aligned with the actual similarity of the generated summaries to human-written ones. However, by applying Platt scaling, the authors are able to significantly improve the calibration of the LLMs, achieving high skill scores and low Brier scores.

In contrast, the authors find that the LLMs' self-reflective confidence measures (logit-based and verbalized) are not well-calibrated. The paper provides insights into the challenges of calibration for generative tasks like code summarization, where the evaluation metrics are continuous rather than binary.

Estatísticas

The average token probability of LLM-generated summaries has a Spearman rank correlation of 0.03 to 0.45 with various summary evaluation metrics.
The SentenceBERT metric has an AUC-ROC of 0.903 in distinguishing human-like summaries from non-human-like ones.
The raw Brier score for the LLMs ranges from 0.30 to 0.67, indicating poor calibration.
After Platt scaling, the Brier score improves to 0.03 to 0.09, indicating well-calibrated models.
The skill score for the rescaled models ranges from 0.05 to 0.24, indicating significant improvement in calibration.

Citações

"LLMs often err and generate something quite unlike what a human might say."
"Given an LLM-produced code summary, is there a way to gauge whether it's likely to be sufficiently similar to a human produced summary, or not?"

Principais Insights Extraídos De

Enhancing Trust in LLM-Generated Code Summaries with Calibrated Confidence Scores

by Yuvraj Virk,... às arxiv.org 05-01-2024

https://arxiv.org/pdf/2404.19318.pdf

Perguntas Mais Profundas

How can the calibration of LLM-generated code summaries be further improved beyond Platt scaling?

Calibration of LLM-generated code summaries can be further improved by exploring alternative rescaling techniques such as isotonic regression or histogram binning. Isotonic regression is a non-parametric method that can adjust the predicted probabilities to better align with the observed frequencies of events. This technique can be particularly useful in cases where Platt scaling may not be as effective. Additionally, histogram binning can be employed to group the predicted probabilities into bins and adjust them accordingly, providing a more accurate calibration of the model's confidence levels. By experimenting with these different rescaling methods, researchers can potentially enhance the reliability and accuracy of LLM-generated code summaries beyond what Platt scaling alone can achieve.

What are the implications of poor calibration in LLM-generated code summaries for software development and maintenance tasks?

Poor calibration in LLM-generated code summaries can have significant implications for software development and maintenance tasks. Firstly, inaccurate or unreliable summaries can lead to misunderstandings of the codebase, potentially resulting in errors or inefficiencies during development and maintenance. Developers may rely on these summaries to comprehend complex code structures, and if the summaries are not calibrated correctly, it can lead to incorrect assumptions or actions. This can ultimately impact the quality and reliability of the software being developed or maintained. Additionally, poor calibration can erode trust in AI-generated summaries, leading to reluctance in adopting automated tools for code summarization. Overall, the implications of poor calibration can hinder productivity, introduce risks, and impede the effectiveness of software development and maintenance processes.

How can the intent and purpose of human-written code summaries be better captured in the evaluation of LLM-generated summaries?

To better capture the intent and purpose of human-written code summaries in the evaluation of LLM-generated summaries, researchers can consider incorporating more nuanced evaluation metrics that go beyond simple similarity measures. One approach could involve developing metrics that assess the relevance and context of the summary in relation to the code snippet. For example, metrics that evaluate the informativeness, clarity, and conciseness of the summary can provide a more comprehensive understanding of how well the LLM-generated summary aligns with the human-written summary. Additionally, incorporating user studies or feedback from developers can offer valuable insights into the perceived usefulness and effectiveness of the summaries in real-world scenarios. By integrating a combination of qualitative and quantitative evaluation methods that focus on capturing the holistic nature of human-written summaries, researchers can enhance the evaluation process and ensure that LLM-generated summaries align more closely with the intent and purpose of their human-written counterparts.