ClarifyGPT: A Framework for Enhancing LLM-Based Code Generation via Requirements Clarification
Bibliographic record
Abstract
Large Language Models (LLMs), such as ChatGPT, have demonstrated impressive capabilities in automatically generating code from provided natural language requirements. However, in real-world practice, it is inevitable that the requirements written by users might be ambiguous or insufficient. Current LLMs will directly generate programs according to those unclear requirements, regardless of interactive clarification, which will likely deviate from the original user intents. To bridge that gap, we introduce a novel framework named C larify GPT, which aims to enhance code generation by empowering LLMs with the ability to identify ambiguous requirements and ask targeted clarifying questions. Specifically, C larify GPT first detects whether a given requirement is ambiguous by performing a code consistency check. If it is ambiguous, C larify GPT prompts an LLM to generate targeted clarifying questions. After receiving question responses, C larify GPT refines the ambiguous requirement and inputs it into the same LLM to generate a final code solution. To evaluate our C larify GPT, we invite ten participants to use C larify GPT for code generation on two benchmarks: MBPP-sanitized and MBPP-ET. The results show that C larify GPT elevates the performance (Pass@1) of GPT-4 from 70.96% to 80.80% on MBPP-sanitized. Furthermore, to conduct large-scale automated evaluations of C larify GPT across different LLMs and benchmarks without requiring user participation, we introduce a high-fidelity simulation method to simulate user responses. The results demonstrate that C larify GPT can significantly enhance code generation performance compared to the baselines. In particular, C larify GPT improves the average performance of GPT-4 and ChatGPT across five benchmarks from 62.43% to 69.60% and from 54.32% to 62.37%, respectively. A human evaluation also confirms the effectiveness of C larify GPT in detecting ambiguous requirements and generating high-quality clarifying questions. We believe that C larify GPT can effectively facilitate the practical application of LLMs in real-world development environments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".