Evaluating Consistency and Reasoning Capabilities of Large Language Models

📄 arXiv: 2404.16478v1 📥 PDF

作者: Yash Saxena, Sarthak Chopra, Arunendra Mani Tripathi

分类: cs.CL, cs.AI

发布日期: 2024-04-25

💡 一句话要点

评估大型语言模型的一致性与推理能力

🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)

关键词: 大型语言模型 一致性评估 推理能力 Boolq数据集 模型比较 自然语言处理 人工智能

📋 核心要点

  1. 现有大型语言模型在生成一致性和推理能力方面存在显著不足，常常导致不准确的响应。
  2. 本文通过使用Boolq数据集，评估公共与专有LLMs的一致性和推理能力，提出了一种系统的比较方法。
  3. 实验结果显示，专有模型在一致性和推理能力上表现优于公共模型，但均未达到90%的高标准。

📝 摘要（中文）

大型语言模型（LLMs）在学术、研究、商业和金融等多个领域被广泛应用于文本生成、摘要和翻译等任务。尽管其应用广泛，这些模型仍然常常产生不正确和误导性的信息，表现出幻觉倾向。本文旨在评估和比较公共与专有LLMs的一致性和推理能力，利用Boolq数据集作为基准，通过对模型生成的响应与真实答案进行对比，分析其推理能力和一致性。研究发现，专有模型在一致性和推理能力上通常优于公共模型，但即使在基本常识问题上，所有模型的得分均未达到90%。

🔬 方法详解

问题定义：本文解决大型语言模型在一致性和推理能力方面的不足，现有方法常常导致不准确的输出和缺乏连贯的推理。

核心思路：通过使用Boolq数据集对模型的输出进行系统评估，比较不同模型在一致性和推理能力上的表现，旨在揭示模型的潜在缺陷。

技术框架：研究首先将Boolq数据集中的问题作为提示输入到模型中，然后评估生成的响应与真实答案的匹配程度，同时分析模型生成的解释以评估推理能力。

关键创新：本研究的创新在于系统性地比较公共与专有模型的一致性和推理能力，揭示了模型之间的性能差异及其原因。

关键设计：在实验中，使用BERT、BLEU和F-1等指标来量化模型的推理能力，并通过重复提问来评估一致性，确保评估的全面性和准确性。

🖼️ 关键图片

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📊 实验亮点

实验结果表明，专有模型在一致性和推理能力上普遍优于公共模型，但所有模型在基本常识问题上的得分均未达到90%。这一发现强调了当前语言模型在推理方面的挑战，指向未来改进的方向。

🎯 应用场景

该研究的潜在应用领域包括教育、客服和内容生成等，能够帮助开发更为可靠和一致的语言模型，提升用户体验。未来，改进的推理能力将推动智能助手和自动化系统的广泛应用，促进人机交互的自然性和有效性。

📄 摘要（原文）

Large Language Models (LLMs) are extensively used today across various sectors, including academia, research, business, and finance, for tasks such as text generation, summarization, and translation. Despite their widespread adoption, these models often produce incorrect and misleading information, exhibiting a tendency to hallucinate. This behavior can be attributed to several factors, with consistency and reasoning capabilities being significant contributors. LLMs frequently lack the ability to generate explanations and engage in coherent reasoning, leading to inaccurate responses. Moreover, they exhibit inconsistencies in their outputs. This paper aims to evaluate and compare the consistency and reasoning capabilities of both public and proprietary LLMs. The experiments utilize the Boolq dataset as the ground truth, comprising questions, answers, and corresponding explanations. Queries from the dataset are presented as prompts to the LLMs, and the generated responses are evaluated against the ground truth answers. Additionally, explanations are generated to assess the models' reasoning abilities. Consistency is evaluated by repeatedly presenting the same query to the models and observing for variations in their responses. For measuring reasoning capabilities, the generated explanations are compared to the ground truth explanations using metrics such as BERT, BLEU, and F-1 scores. The findings reveal that proprietary models generally outperform public models in terms of both consistency and reasoning capabilities. However, even when presented with basic general knowledge questions, none of the models achieved a score of 90\% in both consistency and reasoning. This study underscores the direct correlation between consistency and reasoning abilities in LLMs and highlights the inherent reasoning challenges present in current language models.