Summary Boosting Large Language Models for Code arxiv.org
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One Line
The RRTF framework enhances code language models for code generation, leading to PanGu-Coder2 achieving top performance on various benchmarks.
Slides
Slide Presentation (9 slides)
Key Points
- RRTF (Rank Responses to align Test&Teacher Feedback) is a new framework proposed to boost the performance of pre-trained language models for code generation.
- PanGu-Coder2 is a model developed under the RRTF framework that achieves state-of-the-art performance on multiple benchmarks.
- The reward-ranked fine-tuning (RAFT) technique is introduced to address inefficiency and instability in language models by selecting high-quality model outputs based on a reward model aligned with human preferences.
- The effectiveness of PanGu-Coder2 for code generation is validated through experiments and a survey to ensure no data leakage.
- Benchmarks such as HumanEval, CoderEval, and LeetCode are used to evaluate the performance of large language models in code generation. PanGu-Coder2 outperforms other open-source models across these benchmarks.
- The task of creating a pile of stones with n levels is solved using the make-a-pile function, which determines the number of stones in each level based on whether n is odd or even.
- The input text summarizes various papers and models related to large language models for code generation, including "CERT: Continual pre-training on sketches for library-oriented code generation" and "SantaCoder: don't reach for the stars!"
- The input text also mentions the use of private libraries in language models and generating code by retrieving and reading docs as topics covered in research papers and preprints related to training and improving large language models for code generation.
Summaries
18 word summary
The RRTF framework improves pre-trained code language models for code generation. PanGu-Coder2 achieves state-of-the-art results on multiple benchmarks.
34 word summary
The paper introduces the RRTF framework to enhance the performance of pre-trained code language models (LLMs) for code generation. They present PanGu-Coder2, a model that achieves state-of-the-art results on multiple benchmarks. The authors also
339 word summary
The paper discusses the use of large language models for code generation and proposes a new framework called RRTF (Rank Responses to align Test&Teacher Feedback) to boost the performance of pre-trained models. PanGu-Coder2, developed under this framework
The authors of this document propose a new optimization paradigm called RRTF to improve the code generation performance of pre-trained Code LLMs. They present a model called PanGu-Coder2 that achieves state-of-the-art performance on multiple benchmarks. The
Dong et al. proposed the reward-ranked fine-tuning (RAFT) technique for language models to address inefficiency and instability. The technique selects high-quality model outputs based on a reward model and uses them to train a model aligned with human preferences.
The authors of the paper conducted a survey to ensure that there is no data leakage in their experiments, validating the effectiveness of their proposed PanGu-Coder2 model for code generation. They also introduced the RRTF framework, inspired by RRHF,
Benchmarks for evaluating the performance of large language models (LLMs) in code generation include HumanEval, CoderEval, and LeetCode. HumanEval consists of 164 programming tasks, CoderEval includes 230 functions from open-source Python
PanGu-Coder2 achieves the best results among all open-source models across various benchmarks, outperforming WizardCoder by 4.34% and showing significant improvement over StarCoder. It also performs better than larger models like PaLM-Coder and
Given a positive integer n, the task is to create a pile of stones with n levels. The number of stones in each level depends on whether n is odd or even. To solve this, a function called make-a-pile is implemented. It
This summary provides an overview of various papers and models related to large language models for code generation. The papers mentioned include "CERT: Continual pre-training on sketches for library-oriented code generation," "SantaCoder: don't reach for the stars!,"
This summary provides a list of references to various research papers and preprints related to training and improving large language models for code generation. The papers cover a range of topics such as generating code by retrieving and reading docs, using private libraries in language models,