PanGu-Coder2: Boosting Large Language Models for Code with Ranking Feedback

• URL: http://arxiv.org/abs/2307.14936v1
• Date: Thu, 27 Jul 2023 15:28:29 GMT
• Title: PanGu-Coder2: Boosting Large Language Models for Code with Ranking Feedback
• Authors: Bo Shen, Jiaxin Zhang, Taihong Chen, Daoguang Zan, Bing Geng, An Fu, Muhan Zeng, Ailun Yu, Jichuan Ji, Jingyang Zhao, Yuenan Guo, Qianxiang Wang
• Abstract summary: We propose a novel RRTF (Rank Responses to align Test&Teacher Feedback) framework, which can effectively and efficiently boost pre-trained large language models for code generation. Under this framework, we present PanGu-Coder2, which achieves 62.20% pass@1 on the OpenAI HumanEval benchmark.
• Score: 5.459517921633247
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Large Language Models for Code (Code LLM) are flourishing. New and powerful models are released on a weekly basis, demonstrating remarkable performance on the code generation task. Various approaches have been proposed to boost the code generation performance of pre-trained Code LLMs, such as supervised fine-tuning, instruction tuning, reinforcement learning, etc. In this paper, we propose a novel RRTF (Rank Responses to align Test&Teacher Feedback) framework, which can effectively and efficiently boost pre-trained large language models for code generation. Under this framework, we present PanGu-Coder2, which achieves 62.20% pass@1 on the OpenAI HumanEval benchmark. Furthermore, through an extensive evaluation on CoderEval and LeetCode benchmarks, we show that PanGu-Coder2 consistently outperforms all previous Code LLMs.

Related papers

• Rethinking Code Refinement: Learning to Judge Code Efficiency [60.04718679054704]
  Large Language Models (LLMs) have demonstrated impressive capabilities in understanding and generating codes. We propose a novel method based on the code language model that is trained to judge the efficiency between two different codes. We validate our method on multiple programming languages with multiple refinement steps, demonstrating that the proposed method can effectively distinguish between more and less efficient versions of code.
  arXiv  Detail & Related papers  (2024-10-29T06:17:37Z)
• DolphCoder: Echo-Locating Code Large Language Models with Diverse and Multi-Objective Instruction Tuning [36.78560777629329]
  We introduce a diverse instruction model (DolphCoder) with self-evaluating for code generation. It learns diverse instruction targets and combines a code evaluation objective to enhance its code generation ability. Our model achieves superior performance on the HumanEval and MBPP benchmarks.
  arXiv  Detail & Related papers  (2024-02-14T12:34:58Z)
• StepCoder: Improve Code Generation with Reinforcement Learning from Compiler Feedback [58.20547418182074]
  We introduce StepCoder, a novel framework for code generation, consisting of two main components. CCCS addresses the exploration challenge by breaking the long sequences code generation task into a Curriculum of Code Completion Subtasks. FGO only optimize the model by masking the unexecuted code segments to provide Fine-Grained Optimization. Our method improves the ability to explore the output space and outperforms state-of-the-art approaches in corresponding benchmarks.
  arXiv  Detail & Related papers  (2024-02-02T13:14:31Z)
• JumpCoder: Go Beyond Autoregressive Coder via Online Modification [18.9350072969148]
  We introduce JumpCoder, a novel model-agnostic framework that enables human-like online modification and non-sequential generation to augment code LLMs. The key idea behind JumpCoder is to insert new code into the currently generated code when necessary during generation, which is achieved through an auxiliary infilling model.
  arXiv  Detail & Related papers  (2024-01-15T18:04:29Z)
• CodeFuse-13B: A Pretrained Multi-lingual Code Large Language Model [58.127534002232096]
  This paper introduces CodeFuse-13B, an open-sourced pre-trained code LLM. It is specifically designed for code-related tasks with both English and Chinese prompts. CodeFuse achieves its effectiveness by utilizing a high quality pre-training dataset.
  arXiv  Detail & Related papers  (2023-10-10T02:38:44Z)
• Exploring Continual Learning for Code Generation Models [80.78036093054855]
  Continual Learning (CL) is an important aspect that remains underexplored in the code domain. We introduce a benchmark called CodeTask-CL that covers a wide range of tasks, including code generation, translation, summarization, and refinement. We find that effective methods like Prompt Pooling (PP) suffer from catastrophic forgetting due to the unstable training of the prompt selection mechanism.
  arXiv  Detail & Related papers  (2023-07-05T16:58:39Z)
• CodeT5+: Open Code Large Language Models for Code Understanding and Generation [72.1638273937025]
  Large language models (LLMs) pretrained on vast source code have achieved prominent progress in code intelligence. CodeT5+ is a family of encoder-decoder LLMs for code in which component modules can be flexibly combined to suit a wide range of downstream code tasks. We extensively evaluate CodeT5+ on over 20 code-related benchmarks in different settings, including zero-shot, finetuning, and instruction-tuning.
  arXiv  Detail & Related papers  (2023-05-13T14:23:07Z)
• Stochastic Code Generation [1.7205106391379026]
  Large language models pre-trained for code generation can generate high-quality short code but often struggle with generating coherent long code. This issue is also observed in language modeling for long text generation. In this study, we investigate whether this technique can be applied to code generation to improve coherence.
  arXiv  Detail & Related papers  (2023-04-14T00:01:05Z)
• Better Language Models of Code through Self-Improvement [18.75015225501755]
  We propose a simple data augmentation framework for pre-trained language models for code (PLMCs) Our framework utilizes knowledge gained during the pre-training and fine-tuning stage to generate pseudo data, which is then used as training data for the next step. The results show that our framework significantly improves PLMCs' performance in code-related sequence generation tasks.
  arXiv  Detail & Related papers  (2023-04-02T10:59:19Z)
• CodeRetriever: Unimodal and Bimodal Contrastive Learning [128.06072658302165]
  We propose the CodeRetriever model, which combines the unimodal and bimodal contrastive learning to train function-level code semantic representations. For unimodal contrastive learning, we design a semantic-guided method to build positive code pairs based on the documentation and function name. For bimodal contrastive learning, we leverage the documentation and in-line comments of code to build text-code pairs.
  arXiv  Detail & Related papers  (2022-01-26T10:54:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.