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Found 11 papers, 7 papers with code
LLMs as Continuous Learners: Improving the Reproduction of Defective Code in Software Issues
Reproducing buggy code is the first and crucially important step in issue resolving, as it aids in identifying the underlying problems and validating that generated patches resolve the problem.
UTMath: Math Evaluation with Unit Test via Reasoning-to-Coding Thoughts
The evaluation of mathematical reasoning capabilities is essential for advancing Artificial General Intelligence (AGI).
Lingma SWE-GPT: An Open Development-Process-Centric Language Model for Automated Software Improvement
The results demonstrate that Lingma SWE-GPT 72B successfully resolves 30. 20% of the GitHub issues, marking a significant improvement in automatic issue resolution (22. 76% relative improvement compared to Llama 3. 1 405B), approaching the performance of closed-source models (31. 80\% issues of GPT-4o resolved).
Codev-Bench: How Do LLMs Understand Developer-Centric Code Completion?
Code completion, a key downstream task in code generation, is one of the most frequent and impactful methods for enhancing developer productivity in software development.
In-Context Transfer Learning: Demonstration Synthesis by Transferring Similar Tasks
To address this, inspired by transfer learning, we propose In-Context Transfer Learning (ICTL), which synthesizes target task demonstrations by transferring labeled demonstrations from similar source tasks.
Scaling Offline Model-Based RL via Jointly-Optimized World-Action Model Pretraining
Experimental results indicate that our largest agent, with 150 million parameters, achieves 78. 9% human-level performance on pretrained games using only 10% subsampled offline data, outperforming existing state-of-the-art large-scale offline RL baselines by 31. 6% on averange.
How to Understand Whole Software Repository?
Specifically, we first condense the critical information of the whole repository into the repository knowledge graph in a top-to-down mode to decrease the complexity of repository.
End-to-end Learnable Clustering for Intent Learning in Recommendation
Concretely, we encode user behavior sequences and initialize the cluster centers (latent intents) as learnable neurons.
Bridging Code Semantic and LLMs: Semantic Chain-of-Thought Prompting for Code Generation
Meanwhile, while traditional techniques leveraging such semantic information require complex static or dynamic code analysis to obtain features such as data flow and control flow, SeCoT demonstrates that this process can be fully automated via the intrinsic capabilities of LLMs (i. e., in-context learning), while being generalizable and applicable to challenging domains.
At Which Training Stage Does Code Data Help LLMs Reasoning?
Inspired by the great success of code data in training LLMs, we naturally wonder at which training stage introducing code data can really help LLMs reasoning.
Language-Enhanced Session-Based Recommendation with Decoupled Contrastive Learning
Furthermore, we introduce a novel Decoupled Contrastive Learning method to enhance the effectiveness of the language representation.
