Large language models (LLMs) have garnered considerable attention for their
proficiency in tackling intricate tasks, particularly leveraging their
capacities for zero-shot and in-context learning. However, their utility has
been predominantly restricted to general tasks due to an absence of
domain-specific knowledge. This constraint becomes particularly pertinent in
the realm of protein engineering, where specialized expertise is required for
tasks such as protein function prediction, protein evolution analysis, and
protein design, with a level of specialization that existing LLMs cannot
furnish. In response to this challenge, we introduce \textsc{ProteinEngine}, a
human-centered platform aimed at amplifying the capabilities of LLMs in protein
engineering by seamlessly integrating a comprehensive range of relevant tools,
packages, and software via API calls. Uniquely, \textsc{ProteinEngine} assigns
three distinct roles to LLMs, facilitating efficient task delegation,
specialized task resolution, and effective communication of results. This
design fosters high extensibility and promotes the smooth incorporation of new
algorithms, models, and features for future development. Extensive user
studies, involving participants from both the AI and protein engineering
communities across academia and industry, consistently validate the superiority
of \textsc{ProteinEngine} in augmenting the reliability and precision of deep
learning in protein engineering tasks. Consequently, our findings highlight the
potential of \textsc{ProteinEngine} to bride the disconnected tools for future
research in the protein engineering domain.

ProteinEngine 是一个人为中心的平台，通过 API 调用来集成相关工具、软件和包，分配任务、解决专业任务和有效沟通结果，验证了其在蛋白工程任务中提高深度学习可靠性和精确性的优越性，并展示了在未来蛋白工程研究中整合分散工具的潜力。

ProteinEngine: 通过领域知识强化 LLM 在蛋白工程中的应用

ProteinEngine: Empower LLM with Domain Knowledge for Protein Engineering

Large language models have made significant progress in various language
tasks, yet they still struggle with complex mathematics. In this paper, we
propose ToRA a series of Tool-integrated Reasoning Agents designed to solve
challenging mathematical problems by seamlessly integrating natural language
reasoning with the utilization of external tools (e.g., computation libraries
and symbolic solvers), thereby amalgamating the analytical prowess of language
and the computational efficiency of tools. To train ToRA, we curate interactive
tool-use trajectories on mathematical datasets, apply imitation learning on the
annotations, and propose output space shaping to further refine models'
reasoning behavior. As a result, ToRA models significantly outperform
open-source models on 10 mathematical reasoning datasets across all scales with
13%-19% absolute improvements on average. Notably, ToRA-7B reaches 44.6% on the
competition-level dataset MATH, surpassing the best open-source model
WizardMath-70B by 22% absolute. ToRA-34B is also the first open-source model
that achieves an accuracy exceeding 50% on MATH, which significantly
outperforms GPT-4's CoT result, and is competitive with GPT-4 solving problems
with programs. Additionally, we conduct a comprehensive analysis of the
benefits and remaining challenges of tool interaction for mathematical
reasoning, providing valuable insights for future research.

通过无缝集成自然语言推理与外部工具的使用，我们提出了一系列名为 ToRA 的工具集成推理代理，旨在解决复杂的数学问题，从而融合了语言的分析能力和工具的计算效率。通过在数学数据集上训练 ToRA，我们验证其在 10 个数学推理数据集上显著优于开源模型，取得了 13% 至 19% 的绝对改进。尤其是，在 MATH 竞赛级数据集上，ToRA-7B 达到了 44.6%，超过最好的开源模型 WizardMath-70B 22%。此外，我们还对工具交互在数学推理中的益处和挑战进行了全面分析，为未来的研究提供了有价值的见解。