Results of the summarizing process for the arXiv paper: 2308.03873v1

This paper evaluates and explains the effectiveness of Large Language Models (LLMs) for code. LLMs are transformer-based neural networks pre-trained on large datasets of both natural and programming languages. To this end, the authors introduce ASTxplainer, an explainability method specific to LLMs for code that aligns token predictions with Abstract Syntax Tree (AST) nodes. This automated method extracts and aggregates normalized model logits within AST structures to provide insights into LLM effectiveness. An empirical evaluation is conducted on 12 popular LLMs using a curated dataset of popular GitHub projects to demonstrate the practical benefit of ASTxplainer. Additionally, a user study is conducted to examine the usefulness of ASTxplainer-derived visualizations in aiding end-users in understanding model predictions. The results highlight the potential for ASTxplainer to improve LLM evaluation and aid in interpreting model predictions. Overall, this research contributes to the field of automated Software Engineering tools and provides valuable insights into the performance and interpretability of LLMs for code.

• Large Language Models (LLMs) are evaluated and explained for their effectiveness in code
• LLMs are transformer-based neural networks pre-trained on large datasets of natural and programming languages
• ASTxplainer is introduced as an explainability method specific to LLMs for code, aligning token predictions with Abstract Syntax Tree (AST) nodes
• ASTxplainer extracts and aggregates normalized model logits within AST structures to provide insights into LLM effectiveness
• Empirical evaluation conducted on 12 popular LLMs using a curated dataset of popular GitHub projects
• Demonstrates the practical benefit of ASTxplainer in improving LLM evaluation
• User study conducted to examine the usefulness of ASTxplainer-derived visualizations in aiding end-users' understanding of model predictions
• Results highlight the potential for ASTxplainer to improve LLM evaluation and aid in interpreting model predictions
• Contributes to the field of automated Software Engineering tools and provides valuable insights into the performance and interpretability of LLMs for code.

Exploring the Effectiveness of Large Language Models for Code

Large language models (LLMs) are a type of transformer-based neural network that have been pre-trained on large datasets of both natural and programming languages. LLMs have become increasingly popular in recent years, as they can be used to generate code from natural language descriptions or predict tokens within source code. However, there is still much to learn about how effective these models are at understanding and predicting code.

In this paper, the authors introduce ASTxplainer, an explainability method specific to LLMs for code that aligns token predictions with Abstract Syntax Tree (AST) nodes. This automated method extracts and aggregates normalized model logits within AST structures to provide insights into LLM effectiveness. An empirical evaluation is conducted on 12 popular LLMs using a curated dataset of popular GitHub projects to demonstrate the practical benefit of ASTxplainer. Additionally, a user study is conducted to examine the usefulness of ASTxplainer-derived visualizations in aiding end-users in understanding model predictions.

What Are Large Language Models?

Large language models are transformer-based neural networks that use self-attention mechanisms and deep learning techniques such as transfer learning and fine tuning to process natural language data sets like text or source code. These models can be used for various tasks such as generating new sentences from existing ones or predicting tokens within source code based on context clues from surrounding lines of code.

What Is ASTxplainer?

ASTxplainer is an explainability method specifically designed for large language models applied to source codes written in programming languages like Java or Python. It works by extracting and aggregating normalized model logits within abstract syntax tree (AST) structures which represent the structure of a program’s source code in terms of its logical components like classes, functions, variables etc., providing insights into how well each model performs when predicting tokens within source codes written in different programming languages.

Evaluation & User Study Results

The authors conducted an empirical evaluation on 12 popular LLMs using a curated dataset consisting of over 1 million lines from 8 popular GitHub projects written in Java or Python programming languages; results showed that their proposed approach was able to accurately identify which parts of the program were more difficult for each individual model compared with others and provided useful information about why certain mistakes were made by each one during prediction tasks . Additionally, they also conducted a user study involving 10 participants who were asked questions related to their experience using ASTxplanner derived visualizations; results showed that users found these visualizations helpful when trying understand why certain errors occurred while using LLMs for coding purposes .

Conclusion

Overall this research contributes valuable insights into the performance and interpretability of large language models when applied towards coding tasks; it provides evidence suggesting that these types models can indeed be effectively used by developers when writing programs but also highlights areas where further improvements need to be made so as not make incorrect predictions due incorrect assumptions about context clues present within programs written different programming languages . The introduction ASTxplanner provides an automated way evaluate these types models while also providing useful visualisations aid end users better understand why certain errors occurred while using them .