This paper systematically explores how Large Language Models (LLMs) generate
explanations of code examples of the type used in intro-to-programming courses.
As we show, the nature of code explanations generated by LLMs varies considerably
based on the wording of the prompt, the target code examples being explained,
the programming language, the temperature parameter, and the version of the
LLM. Nevertheless, they are consistent in two major respects for Java and Python:
the readability level, which hovers around 7-8 grade, and lexical density, i.e., the
relative size of the meaninful words with respect to the total explanation size.
Furthermore, the explanations score very high in correctness but less on three other
metrics: completeness, conciseness, and contextualization.
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Large Language Models Help Humans Verify Truthfulness -- Except When They Are Convincingly Wrong
Large Language Models (LLMs) are increasingly used for accessing information on the web. Their truthfulness and factuality are thus of great interest. To help users make the right decisions about the information they get, LLMs should not only provide information but also help users fact-check it. Our experiments with 80 crowdworkers compare language models with search engines (information retrieval systems) at facilitating fact-checking. We prompt LLMs to validate a given claim and provide corresponding explanations. Users reading LLM explanations are significantly more efficient than those using search engines while achieving similar accuracy. However, they over-rely on the LLMs when the explanation is wrong. To reduce over-reliance on LLMs, we ask LLMs to provide contrastive information - explain both why the claim is true and false, and then we present both sides of the explanation to users. This contrastive explanation mitigates users' over-reliance on LLMs, but cannot significantly outperform search engines. Further, showing both search engine results and LLM explanations offers no complementary benefits compared to search engines alone. Taken together, our study highlights that natural language explanations by LLMs may not be a reliable replacement for reading the retrieved passages, especially in high-stakes settings where over-relying on wrong AI explanations could lead to critical consequences.
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- Award ID(s):
- 2229885
- NSF-PAR ID:
- 10522356
- Publisher / Repository:
- 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2024)
- Date Published:
- Format(s):
- Medium: X
- Location:
- Mexico City, Mexico
- Sponsoring Org:
- National Science Foundation
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This paper systematically investigates the generation of code explanations by Large Language Models (LLMs) for code examples commonly encountered in introductory programming courses. Our findings reveal significant variations in the nature of code explanations produced by LLMs, influenced by factors such as the wording of the prompt, the specific code examples under consideration, the programming language involved, the temperature parameter, and the version of the LLM. However, a consistent pattern emerges for Java and Python, where explanations exhibit a Flesch-Kincaid readability level of approximately 7-8 grade and a consistent lexical density, indicating the proportion of meaningful words relative to the total explanation size. Additionally, the generated explanations consistently achieve high scores for correctness, but lower scores on three other metrics: completeness, conciseness, and specificity.more » « less
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Accepted Manuscript1.0
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