Are LLMs Good Literature Review Writers? Evaluating the Literature Review Writing Ability

本文是LLM系列文章,针对《Are LLMs Good Literature Review Writers? Evaluating the Literature Review Writing Ability of Large Language Models》的翻译。

LLM是优秀的文学评论作家吗?大型语言模型文献综述写作能力评价

摘要

文献综述是学术写作的一种重要形式,涉及文献收集、组织和总结的复杂过程。大型语言模型(LLM)的出现引入了有前景的工具来自动化这些过程。然而,他们在撰写综合文献综述方面的实际能力仍然没有得到充分探索,例如他们是否能够生成准确可靠的参考文献。为了弥补这一差距,我们提出了一个自动评估LLM文献综述写作能力的框架。我们评估LLM在三个任务中的表现:生成参考文献、撰写摘要和撰写文献综述。我们采用外部工具进行多维评估,包括评估参考文献中的幻觉率、语义覆盖率以及与人类书面语境的事实一致性。通过分析实验结果,我们发现,尽管取得了进步,但即使是最复杂的模型也无法避免产生幻觉参考。此外,不同的模型在不同学科的文献综述写作中表现出不同的表现。

1 引言

2 相关工作

3 方法

4 实验

5 结论

本文提出了一个评估LLM文献综述写作能力的框架。该框架包括三个

### AI Review Guidelines and Best Practices When conducting reviews or evaluations of AI systems, especially those involving large language models (LLMs), adherence to established guidelines ensures both effectiveness and compliance with legal requirements. The focus should be on best practices for instruction-tuned LLMs[^1]. This involves ensuring the model's outputs are aligned with intended instructions while maintaining high standards of accuracy, safety, and reliability. Privacy concerns play a critical role during an AI review process. Companies must address these issues by protecting customer privacy in accordance with applicable laws, regulations, and guidelines, as well as adhering to ethical standards and best practices[^2]. To effectively evaluate AI systems: #### Establish Clear Objectives Define specific goals for what needs to be reviewed within the system. Determine whether the assessment focuses on performance metrics, security measures, data handling procedures, etc. #### Perform Thorough Testing Conduct comprehensive tests covering various scenarios where the AI might operate under different conditions. Include stress testing, edge case analysis, and real-world simulations to uncover potential weaknesses or biases in decision-making processes. #### Evaluate Ethical Implications Assess how decisions made by the AI could impact society at large. Consider factors like fairness, transparency, accountability, and bias mitigation strategies employed throughout development stages. #### Ensure Regulatory Compliance Verify that all operations comply fully with current legislation concerning personal information protection, intellectual property rights, export controls, among others. #### Implement Continuous Monitoring After deployment, continuously monitor deployed solutions through logging mechanisms capturing input/output pairs alongside metadata about interactions between users and machines over time. ```python def ai_review_process(objective): """ A function simulating key steps involved in reviewing an AI system. Args: objective (str): Specific area being evaluated such as 'performance', 'security'. Returns: str: Summary report highlighting findings from conducted assessments. """ test_results = perform_tests() ethics_assessment = assess_ethics_impact() regulatory_check = check_compliance() summary_report = f""" Objective: {objective} Test Results: {'Passed' if test_results else 'Failed'} Ethics Assessment: {ethics_assessment} Regulatory Check: {'Compliant' if regulatory_check else 'Non-compliant'} """ return summary_report def perform_tests(): # Placeholder logic for actual implementation details... pass def assess_ethics_impact(): # Logic here would involve evaluating societal impacts based on predefined criteria... pass def check_compliance(): # Implementation checks against relevant statutes and industry standards... pass ```
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