Beyond Retrieval: A Multitask Benchmark and Model for Code Search
📰 ArXiv cs.AI
Learn to improve code search with a new multitask benchmark and model, CoREB, that goes beyond retrieval and addresses data contamination and label noise
Action Steps
- Build a code search system using the CoREB benchmark and model to evaluate its performance
- Apply multitask learning to improve the accuracy of code retrieval and reranking
- Configure the CoREB benchmark to test the robustness of your code search system
- Test the CoREB model on your dataset to compare its performance with existing models
- Use the CoREB benchmark to evaluate the effectiveness of different reranking algorithms
Who Needs to Know This
Software engineers and researchers on a team can benefit from this benchmark and model to improve their code search systems, while data scientists and ML engineers can apply the multitask learning approach to other domains
Key Insight
💡 Multitask learning can improve the accuracy of code retrieval and reranking in code search systems
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🚀 Improve code search with CoREB, a new multitask benchmark and model that addresses data contamination and label noise 🚀
Full Article
Title: Beyond Retrieval: A Multitask Benchmark and Model for Code Search
Abstract:
arXiv:2605.04615v1 Announce Type: cross Abstract: Code search has usually been evaluated as first-stage retrieval, even though production systems rely on broader pipelines with reranking and developer-style queries. Existing benchmarks also suffer from data contamination, label noise, and degenerate binary relevance. In this paper, we introduce \textsc{CoREB}, a contamination-limited, multitask \underline{co}de \underline{r}etrieval and r\underline{e}ranking \underline{b}enchmark, together with
Abstract:
arXiv:2605.04615v1 Announce Type: cross Abstract: Code search has usually been evaluated as first-stage retrieval, even though production systems rely on broader pipelines with reranking and developer-style queries. Existing benchmarks also suffer from data contamination, label noise, and degenerate binary relevance. In this paper, we introduce \textsc{CoREB}, a contamination-limited, multitask \underline{co}de \underline{r}etrieval and r\underline{e}ranking \underline{b}enchmark, together with
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