This paper presents Safurai-001, a new Large Language Model (LLM) with
significant potential in the domain of coding assistance. Driven by recent
advancements in coding LLMs, Safurai-001 competes in performance with the
latest models like WizardCoder [Xu et al., 2023], PanguCoder [Shen et al.,
2023] and Phi-1 [Gunasekar et al., 2023] but aims to deliver a more
conversational interaction. By capitalizing on the progress in data engineering
(including latest techniques of data transformation and prompt engineering) and
instruction tuning, this new model promises to stand toe-to-toe with recent
closed and open source developments. Recognizing the need for an efficacious
evaluation metric for coding LLMs, this paper also introduces GPT4-based
MultiParameters, an evaluation benchmark that harnesses varied parameters to
present a comprehensive insight into the models functioning and performance.
Our assessment shows that Safurai-001 can outperform GPT-3.5 by 1.58% and
WizardCoder by 18.78% in the Code Readability parameter and more.

Safurai-001 是一个新型的大型语言模型，具有显著的编码辅助潜力。通过在数据工程方面的进展以及指令优化，它旨在提供更具对话性的互动。该论文还介绍了基于 GPT4 的多参数评估基准，以全面了解模型的功能和性能。评估结果表明，Safurai-001 在代码可读性参数上优于 GPT-3.5 和 WizardCoder 等模型。

Safurai 001：面向代码 LLM 评估的新定性方法

Safurai 001: New Qualitative Approach for Code LLM Evaluation

ChatGPT, a recently developed product by openAI, is successfully leaving its
mark as a multi-purpose natural language based chatbot. In this paper, we are
more interested in analyzing its potential in the field of computational
biology. A major share of work done by computational biologists these days
involve coding up Bioinformatics algorithms, analyzing data, creating
pipelining scripts and even machine learning modeling & feature extraction.
This paper focuses on the potential influence (both positive and negative) of
ChatGPT in the mentioned aspects with illustrative examples from different
perspectives. Compared to other fields of Computer Science, Computational
Biology has - (1) less coding resources, (2) more sensitivity and bias issues
(deals with medical data) and (3) more necessity of coding assistance (people
from diverse background come to this field). Keeping such issues in mind, we
cover use cases such as code writing, reviewing, debugging, converting,
refactoring and pipelining using ChatGPT from the perspective of computational
biologists in this paper.

在本文中，我们着重分析了 ChatGPT 在计算生物学领域的潜力，讨论了它对 Bioinformatics 算法编写、数据分析、脚本编排以及机器学习建模和特征提取等方面的积极和消极影响，并给出了来自不同视角的具体示例。