Zero-shot prompting evaluation reveals optimal strategies for LLM-based programming feedback
Simple prompts beat complex ones: LLMs give better coding feedback with less explicit instructions
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https://arxiv.org/abs/2412.15702
Original Problem 🔍:
Insufficient research exists on optimizing LLM prompts for programming feedback. Current evaluations show contradictory results about prompt engineering's impact across different tasks.
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Solution in this Paper 🛠️:
→ Developed an evaluation framework to assess different zero-shot prompting methods systematically.
→ Tested Chain of Thought, Prompt Chaining, Tree of Thought, and ReAct prompting against a vanilla model.
→ Used five common R programming errors as test cases: directory issues, missing packages, unexecuted code, typos, and naming inconsistencies.
→ Applied Ryan's feedback framework to evaluate response quality across detection, description, explanation, and solution aspects.
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Key Insights from this Paper:
→ Stepwise procedure prompts increase precision in feedback
→ Omitting explicit data specifications improves error identification
→ Trade-off exists between precision and error detection capabilities
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Results 📊:
→ All prompts achieved performance ratings above 0.85
→ Chain of Thought excelled in precision without explicit specifications
→ Variable naming and directory errors showed consistent performance across methods
→ Typos and unexecuted code detection varied significantly between methods
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