While fine-tuned large language models (LLMs) excel in generating
grammatically valid SQL in Text-to-SQL parsing, they often struggle to ensure
semantic accuracy in queries, leading to user confusion and diminished system
usability. To tackle this challenge, we introduce SQLFixAgent, an innovative
multi-agent collaborative framework designed for detecting and repairing
erroneous SQL. Our framework comprises a core agent, SQLRefiner, alongside two
auxiliary agents: SQLReviewer and QueryCrafter. The SQLReviewer agent employs
the rubber duck debugging method to identify potential semantic mismatches
between SQL statement and user query. If the error is detected, the
QueryCrafter agent generates multiple SQL statements as candidate repairs using
a fine-tuned SQLTool. Subsequently, leveraging similar repair retrieval and
failure memory reflexion, the SQLRefiner agent selects the most fitting SQL
statement from the candidates as the final repair. We evaluated our proposed
framework on five Text-to-SQL benchmarks. The experimental results show that
our method consistently enhances the performance of the baseline model,
specifically achieving an execution accuracy improvement of over 3\% on the
Bird benchmark. Our framework also has a higher token efficiency compared to
other advanced methods, making it more competitive.

通过引入 SQLFixAgent 创新的多智能体协作框架，我们解决了大型预训练语言模型在生成 SQL 时语法合理但语义不准确的问题，从而提高了用户理解和系统可用性。

SQLFixAgent: 多智能体协作的语义准确 SQL 生成

SQLFixAgent: Towards Semantic-Accurate SQL Generation via Multi-Agent  Collaboration

This paper presents a novel method for utilizing fine-tuned Large Language
Models (LLMs) to minimize data requirements in load profile analysis,
demonstrated through the restoration of missing data in power system load
profiles. A two-stage fine-tuning strategy is proposed to adapt a pre-trained
LLMs, i.e., GPT-3.5, for missing data restoration tasks. Through empirical
evaluation, we demonstrate the effectiveness of the fine-tuned model in
accurately restoring missing data, achieving comparable performance to
state-of-the-art specifically designed models such as BERT-PIN. Key findings
include the importance of prompt engineering and the optimal utilization of
fine-tuning samples, highlighting the efficiency of few-shot learning in
transferring knowledge from general user cases to specific target users.
Furthermore, the proposed approach demonstrates notable cost-effectiveness and
time efficiency compared to training models from scratch, making it a practical
solution for scenarios with limited data availability and computing resources.
This research has significant potential for application to other power system
load profile analysis tasks. Consequently, it advances the use of LLMs in power
system analytics, offering promising implications for enhancing the resilience
and efficiency of power distribution systems.

该研究提出了一种利用经过微调的大型语言模型（LLMs）来最小化负荷数据要求的新方法，并通过在电力系统负荷配置文件中恢复丢失数据来进行验证。通过实证评估，我们展示了经过微调的模型在准确恢复丢失数据方面的有效性，实现了与 BERT-PIN 等专门设计的模型相当的性能。重要发现包括提示工程的重要性和合理利用微调样本，在从普通用户案例向特定目标用户传递知识方面展示了少样本学习的效率。此外，所提出的方法与从头训练模型相比，具有显著的成本效益和时间效率，在数据有限和计算资源有限的情况下，是一种实际可行的解决方案。该研究对于其他电力系统负荷配置文件分析任务具有重要的应用潜力，推动了在电力系统分析中使用 LLMs 的发展，为提高电力分配系统的韧性和效率提供了有希望的启示。

应用调优 LLMs 减少负载曲线分析的数据需求

Applying Fine-Tuned LLMs for Reducing Data Needs in Load Profile  Analysis

This research pioneers the use of fine-tuned Large Language Models (LLMs) to
automate Systematic Literature Reviews (SLRs), presenting a significant and
novel contribution in integrating AI to enhance academic research
methodologies. Our study employed the latest fine-tuning methodologies together
with open-sourced LLMs, and demonstrated a practical and efficient approach to
automating the final execution stages of an SLR process that involves knowledge
synthesis. The results maintained high fidelity in factual accuracy in LLM
responses, and were validated through the replication of an existing
PRISMA-conforming SLR. Our research proposed solutions for mitigating LLM
hallucination and proposed mechanisms for tracking LLM responses to their
sources of information, thus demonstrating how this approach can meet the
rigorous demands of scholarly research. The findings ultimately confirmed the
potential of fine-tuned LLMs in streamlining various labor-intensive processes
of conducting literature reviews. Given the potential of this approach and its
applicability across all research domains, this foundational study also
advocated for updating PRISMA reporting guidelines to incorporate AI-driven
processes, ensuring methodological transparency and reliability in future SLRs.
This study broadens the appeal of AI-enhanced tools across various academic and
research fields, setting a new standard for conducting comprehensive and
accurate literature reviews with more efficiency in the face of ever-increasing
volumes of academic studies.

本研究探索使用精细调整的大型语言模型（LLMs）自动化系统性文献综述（SLRs），提出了将人工智能与学术研究方法相结合的重要和创新贡献。通过采用最新的精细调整方法和开源的 LLMs，我们展示了一种实用高效的自动化 SLR 过程的方法，包括知识综合的最终执行阶段。研究结果在 LLM 响应的事实准确性方面保持了很高的保真度，并通过对现有符合 PRISMA 的 SLR 的复制进行了验证。研究提出了减轻 LLM 虚幻感的解决方案，并提出了追踪 LLM 响应与信息来源的机制，从而证明了这种方法如何满足学术研究的严格需求。最终的发现证实了精细调整的 LLMs 在简化各种劳动密集型的文献综述过程方面的潜力。鉴于这种方法的潜力及其在所有研究领域的适用性，这项基础研究还呼吁更新 PRISMA 报告指南以整合 AI 驱动的过程，确保未来 SLRs 的方法透明性和可靠性。该研究拓宽了 AI 增强工具在各学术和研究领域的应用，为在日益增长的学术研究数量面前以更高效的方式进行全面准确的文献综述设立了新的标准。