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Cheaper LLM labelling reportedly streamlines commit classification process
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A project utilized a low-cost LLM for labelling commits as maintenance or new development, achieving satisfactory results.
The use of a cheaper LLM for labelling can significantly reduce costs for software projects needing classification. This approach allows for scalable commit classification while maintaining accuracy, which is essential for efficient development workflows.
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A low-cost LLM, GPT 5.6 Luna, was effective in classifying commits.
The implementation involves a loop to manage LLM requests, with a retry mechanism for robustness.
A hybrid approach with a fast classifier improves efficiency by handling obvious cases.
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The shift to using a cheaper LLM for labelling commits as either maintenance or new development represents a practical advancement in software project management. By leveraging GPT 5.6 Luna, the project demonstrated that accurate classifications could be achieved at lower costs, allowing for broader application in similar contexts.
The outlined approach involves calling the LLM for each commit, which incurs a latency of approximately 1.5 seconds per call. While the cost per request is low, this latency could become a bottleneck when labelling large sets of commits, such as the 21,000 mentioned. For projects prioritizing speed, adjustments in how requests are handled may be necessary.
Incorporating a fast classifier alongside the LLM serves to enhance performance by quickly classifying straightforward cases. This hybrid model makes it feasible to reduce the number of calls to the LLM, decreasing overall processing time and increasing efficiency in handling large datasets.
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