DemoCraft: Using In-Context Learning to Improve Code Generation in Large Language Models

• URL: http://arxiv.org/abs/2411.00865v1
• Date: Wed, 30 Oct 2024 19:45:50 GMT
• Title: DemoCraft: Using In-Context Learning to Improve Code Generation in Large Language Models
• Authors: Nirmal Joshua Kapu, Mihit Sreejith,
• Abstract summary: We propose DemoCraft, which enhances code generation by leveraging in-context learning and demonstration selection. Latent concept learning introduces additional concept tokens, which are trainable embeddings that capture task-specific knowledge. Our experimental results demonstrate that the proposed system achieves an approximate 2x increase in the pass@k metric. Our empirical studies indicate that our system attains nearly a 3x improvement in these metrics as well.
• Score: 0.0
• License:
• Abstract: Generating executable code from natural language instructions using Large Language Models (LLMs) poses challenges such as semantic ambiguity and understanding taskspecific contexts. To address these issues, we propose a system called DemoCraft, which enhances code generation by leveraging in-context learning and demonstration selection, combined with latent concept learning. Latent concept learning introduces additional concept tokens, which are trainable embeddings that capture task-specific knowledge. We then test our system on two major datasets: MBPP and Humaneval. Our experimental results demonstrate that the proposed system achieves an approximate 2x increase in the pass@k metric compared to baseline models. Furthermore, we introduce two novel evaluation metrics: correctness@k and similarity@k. Our empirical studies indicate that our system attains nearly a 3x improvement in these metrics as well.

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