Summary of Evaluating Long Range Dependency Handling in Code Generation Models Using Multi-step Key Retrieval, by Yannick Assogba et al.
Evaluating Long Range Dependency Handling in Code Generation Models using Multi-Step Key Retrieval
by Yannick Assogba, Donghao Ren
First submitted to arxiv on: 23 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the ability of code generation models to handle large context sizes, specifically focusing on their capacity to retrieve information from prompts with lengths up to 8k tokens. The authors design a suite of multi-step key retrieval tasks to evaluate model performance, showcasing a significant degradation in performance (up to 2x) when functions reference each other in the prompt. They also identify limitations in models that employ sliding window attention mechanisms and propose simple prompt modifications using call graph information to improve performance up to 3x. This analysis highlights the importance of long-context performance in code completion tools. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper studies how well language models can use big chunks of text to help them make good choices. The researchers created some special tests to see which models do better with really long texts (up to 8,000 words). They found that when functions talk about other functions they haven’t seen before, the models get much worse (up to 2 times worse!). They also saw that some models struggle when they need to look far ahead in the text. To fix this, they came up with simple ways to make the prompts better, which helped the models do up to 3 times better! This helps us understand how to make code completion tools better. |
Keywords
» Artificial intelligence » Attention » Prompt
