**FT2Ra: A Fine-Tuning-Inspired Approach to Retrieval-Augmented Code Completion** **Authors:** Qi Guo, Xiaohong Li, Xiaofei Xie, Shangqing Liu, Ze Tang, Ruitao Feng, Junjie Wang, Jidong Ge, Lei Bu **Abstract:** The rise of code pre-trained models has significantly enhanced various coding tasks, but the large size of these models poses challenges for fine-tuning. Retrieval-based methods have emerged as a promising alternative, but they often rely on heuristics. This paper presents FT2Ra, a novel retrieval-based method inspired by fine-tuning processes. FT2Ra aims to mimic genuine fine-tuning by adopting a learning rate and multi-epoch retrievals. Theoretical analysis highlights the importance of Alogits in improving model predictions. Comprehensive evaluations in token-level and line-level code completions demonstrate FT2Ra's effectiveness, achieving significant improvements over state-of-the-art methods. Even without actual fine-tuning, FT2Ra exhibits competitive performance. **Contributions:** 1. **Theoretical Analysis:** Insights into effective retrieval information and its exploitation. 2. **Methodology:** Introduction of FT2Ra, a retrieval-augmentation technique that emulates fine-tuning. 3. **Comprehensive Evaluation:** Extensive evaluation on token-level and line-level code completion tasks. 4. **Open-Source Resources:** Public availability of data, experimental findings, and tools. **Keywords:** Code Completion, Retrieval-Augmented Language Models **Background and Problem:** - **Retrieval-Augmented Language Models (RaLMs):** Augment pre-trained models with external knowledge. - **Challenges:** Identifying and utilizing retrieved information effectively. ** Approach:** - **Theoretical Analysis:** Derives insights from fine-tuning processes. - **FT2Ra Algorithm:** Incorporates Alogits and iterative retrieval cycles to improve predictions. **Experimental Setup:** - **Benchmarks:** Token-level and line-level completions on pre-trained and fine-tuned models. - **Datasets:** kNN-LM and CodeXGLUE. - **Models:** CodeGPT, UniXcoder. - **Baselines:** kNN-LM, kNN-LM, BM25, ReACC. **Results:** - **Effectiveness:** FT2Ra outperforms baselines in both token-level and line-level completions. - **Comparison with Fine-Tuning:** FT2Ra achieves similar performance to fine-tuned models without fine-tuning. - **Impact of Weighting Strategy and Neighbors:** Weighting strategy and number of neighbors affect performance. - **Multiple Iterations:** Multiple retrieval rounds enhance performance. **Conclusion:** FT2Ra effectively enhances code completion tasks using retrieval-augmented methods, providing a promising approach to fine-tuning-inspired retrieval-Augmented models.**FT2Ra: A Fine-Tuning-Inspired Approach to Retrieval-Augmented Code Completion** **Authors:** Qi Guo, Xiaohong Li, Xiaofei Xie, Shangqing Liu, Ze Tang, Ruitao Feng, Junjie Wang, Jidong Ge, Lei Bu **Abstract:** The rise of code pre-trained models has significantly enhanced various coding tasks, but the large size of these models poses challenges for fine-tuning. Retrieval-based methods have emerged as a promising alternative, but they often rely on heuristics. This paper presents FT2Ra, a novel retrieval-based method inspired by fine-tuning processes. FT2Ra aims to mimic genuine fine-tuning by adopting a learning rate and multi-epoch retrievals. Theoretical analysis highlights the importance of Alogits in improving model predictions. Comprehensive evaluations in token-level and line-level code completions demonstrate FT2Ra's effectiveness, achieving significant improvements over state-of-the-art methods. Even without actual fine-tuning, FT2Ra exhibits competitive performance. **Contributions:** 1. **Theoretical Analysis:** Insights into effective retrieval information and its exploitation. 2. **Methodology:** Introduction of FT2Ra, a retrieval-augmentation technique that emulates fine-tuning. 3. **Comprehensive Evaluation:** Extensive evaluation on token-level and line-level code completion tasks. 4. **Open-Source Resources:** Public availability of data, experimental findings, and tools. **Keywords:** Code Completion, Retrieval-Augmented Language Models **Background and Problem:** - **Retrieval-Augmented Language Models (RaLMs):** Augment pre-trained models with external knowledge. - **Challenges:** Identifying and utilizing retrieved information effectively. ** Approach:** - **Theoretical Analysis:** Derives insights from fine-tuning processes. - **FT2Ra Algorithm:** Incorporates Alogits and iterative retrieval cycles to improve predictions. **Experimental Setup:** - **Benchmarks:** Token-level and line-level completions on pre-trained and fine-tuned models. - **Datasets:** kNN-LM and CodeXGLUE. - **Models:** CodeGPT, UniXcoder. - **Baselines:** kNN-LM, kNN-LM, BM25, ReACC. **Results:** - **Effectiveness:** FT2Ra outperforms baselines in both token-level and line-level completions. - **Comparison with Fine-Tuning:** FT2Ra achieves similar performance to fine-tuned models without fine-tuning. - **Impact of Weighting Strategy and Neighbors:** Weighting strategy and number of neighbors affect performance. - **Multiple Iterations:** Multiple retrieval rounds enhance performance. **Conclusion:** FT2Ra effectively enhances code completion tasks using retrieval-augmented methods, providing a promising approach to fine-tuning-inspired retrieval-Augmented models.