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The widespread use of Large Language Models (LLMs) in software engineering has intensified the need for improved model and resource efficiency.
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AceCoder: An Effective Prompting Technique Specialized in Code Generation
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Starcoder: may the source be with you!
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Compressing Context to Enhance Inference Efficiency of Large Language Models. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing . 6342–6353
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Is your code generated by chatgpt really correct? rigorous evaluation of large language models for code generation
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On Evaluating the Efficiency of Source Code Generated by LLMs. In Proceedings of the 2024 IEEE/ACM First International Conference on AI Foundation Models and Software Engineering . 103–107
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LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression. In Findings of the Association for Computational Linguistics ACL 2024 , Lun-Wei Ku, Andre Martins, and Vivek Srikumar (Eds.). Association for Computational Linguistics, Bangkok, Thailand and virtual meeting, 963–981
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DocuMint: Docstring Generation for Python using Small Language Models
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An empirical study on usage and perceptions of llms in a software engineering project. In Proceedings of the 1st International Workshop on Large Language Models for Code . 111–118
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Don’t Complete It! Preventing Unhelpful Code Completion for Productive and Sustainable Neural Code Completion Systems
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Top Leaderboard Ranking= Top Coding Proficiency, Always? EvoEval: Evolving Coding Benchmarks via LLM
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