This is a Plain English Papers summary of a research paper called Developer vs Model Code Attention: An Eye-Tracking Empirical Study. If you like these kinds of analysis, you should join AImodels.fyi or follow me on Twitter.
Overview
- Recent neural models like OpenAI Codex and AlphaCode have shown impressive code generation abilities due to their attention mechanism.
- However, it's often unclear how these models actually process and reason about code, and how their attention mechanism compares to how developers explore and understand code.
- Understanding the model's reasoning process is important to leverage these models beyond just their raw prediction capabilities.
Plain English Explanation
The paper examines how the attention signals of three large language models for code - CodeGen, InCoder, and GPT-J - align with how developers visually explore and make sense of code. The researchers created an eye-tracking dataset where developers performed code understanding tasks, and then evaluated different ways of processing the models' attention signals to see how well they matched the developers' attention patterns.
The goal is to better understand how these large language models for code actually work under the hood, beyond just their ability to generate code. By seeing how their attention aligns with human attention, the researchers hope to unlock new ways to leverage these models for more effective code exploration and understanding, rather than just raw code generation.
Technical Explanation
The paper examines the attention mechanisms of three open large language models for code - CodeGen, InCoder, and GPT-J - and compares them to how human developers visually explore and make sense of code.
The researchers created an open-source eye-tracking dataset of 92 manually-labeled sessions from 25 developers engaged in code understanding tasks. They then evaluated five heuristic approaches and ten attention-based post-processing methods to see how well the models' attention signals aligned with the developers' gaze patterns.
One novel approach they introduced is "follow-up attention", which exhibited the highest agreement between model and human attention. This method can predict the next line a developer will look at with 47% accuracy, outperforming a baseline of 42.3% that uses the session history of other developers.
The results demonstrate the potential of leveraging the attention signals of pre-trained language models to better understand how they process and reason about code, and to enable more effective code exploration and understanding tools.
Critical Analysis
The paper provides a valuable contribution by empirically studying the alignment between model attention and human attention during code understanding tasks. This offers insights into how these large language models actually process and reason about code, beyond just their raw prediction capabilities.
However, the study is limited to just three specific models - CodeGen, InCoder, and GPT-J. While these are prominent examples, the findings may not generalize to other model architectures or future developments in the field. Additionally, the eye-tracking dataset, while a useful resource, only captures a relatively small number of developers (25) and tasks.
Further research would be needed to validate the findings across a wider range of models, developers, and coding tasks. It would also be interesting to explore how the attention mechanisms of these models evolve as they are fine-tuned or adapted for specific coding domains or applications.
Conclusion
This paper takes an important step towards understanding the inner workings of large language models for code, by examining how their attention signals align with how human developers visually explore and make sense of code. The novel "follow-up attention" approach demonstrated promising results in predicting developers' attention patterns.
These insights could unlock new ways to leverage these powerful models beyond just code generation, enabling more effective code exploration and understanding tools. As the capabilities of language models continue to advance, understanding their reasoning process will be crucial to unlocking their full potential in supporting and augmenting human developers.
If you enjoyed this summary, consider joining AImodels.fyi or following me on Twitter for more AI and machine learning content.
Top comments (0)