DRAK：在LLMs中利用领域特定检索增强知识解锁分子洞察
DRAK: Unlocking Molecular Insights with Domain-Specific Retrieval-Augmented Knowledge in LLMs
AI总结：
针对LLMs在生物分子领域面临的语法与知识鸿沟挑战，提出非参数化知识注入框架DRAK，利用知识感知提示与金标签诱导推理增强领域推理能力，在六项分子任务上超越以往基准，为特定领域知识密集型任务提供统一范式。
摘要：
大型语言模型（LLMs）在特定领域（如生物分子）的独特语法上面临挑战。现有的微调或模态对齐技术难以弥合领域知识鸿沟并理解复杂的分子数据，限制了LLMs在专业领域的进展。为克服这些局限性，我们提出了一种可扩展且适应性强的非参数化知识注入框架，名为Domain-specific Retrieval-Augmented Knowledge (DRAK)，旨在增强特定领域的推理能力。利用知识感知提示和金标签诱导推理，DRAK在分子领域发展出深厚的专业知识，并具备处理广泛分析任务的能力。我们评估了两种不同形式的DRAK变体，证明DRAK在Mol-Instructions数据集内的六项分子任务上超越了以往的基准。大量实验突显了DRAK的强大性能及其解锁分子洞察的潜力，为LLMs在特定领域处理知识密集型任务提供了统一范式。我们的代码即将发布。
英文摘要：
Large Language Models (LLMs) encounter challenges with the unique syntax of specific domains, such as biomolecules. Existing fine-tuning or modality alignment techniques struggle to bridge the domain knowledge gap and understand complex molecular data, limiting LLMs' progress in specialized fields. To overcome these limitations, we propose an expandable and adaptable non-parametric knowledge injection framework named Domain-specific Retrieval-Augmented Knowledge (DRAK), aimed at enhancing reasoning capabilities in specific domains. Utilizing knowledge-aware prompts and gold label-induced reasoning, DRAK has developed profound expertise in the molecular domain and the capability to handle a broad spectrum of analysis tasks. We evaluated two distinct forms of DRAK variants, proving that DRAK exceeds previous benchmarks on six molecular tasks within the Mol-Instructions dataset. Extensive experiments have underscored DRAK's formidable performance and its potential to unlock molecular insights, offering a unified paradigm for LLMs to tackle knowledge-intensive tasks in specific domains. Our code will be available soon.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
