The rise of large language models (LLMs) has revolutionized the way that we interact with artificial intelligence systems through natural language. However, LLMs often misinterpret user queries because of their uncertain intention, leading to less helpful responses. In natural human interactions, clarification is sought through targeted questioning to uncover obscure information. Thus, in this paper, we introduce LaMAI (Language Model with Active Inquiry), designed to endow LLMs with this same level of interactive engagement. LaMAI leverages active learning techniques to raise the most informative questions, fostering a dynamic bidirectional dialogue. This approach not only narrows the contextual gap but also refines the output of the LLMs, aligning it more closely with user expectations. Our empirical studies, across a variety of complex datasets where LLMs have limited conversational context, demonstrate the effectiveness of LaMAI. The method improves answer accuracy from 31.9% to 50.9%, outperforming other leading question-answering frameworks. Moreover, in scenarios involving human participants, LaMAI consistently generates responses that are superior or comparable to baseline methods in more than 82% of the cases. The applicability of LaMAI is further evidenced by its successful integration with various LLMs, highlighting its potential for the future of interactive language models.

翻译：大型语言模型（LLMs）的兴起彻底改变了我们通过自然语言与人工智能系统交互的方式。然而，由于用户查询意图存在不确定性，LLMs常会误解其含义，导致生成响应帮助性不足。在自然人类交互中，人们会通过针对性提问来澄清模糊信息。为此，本文提出LaMAI（具备主动探究能力的语言模型），旨在赋予LLMs同等水平的交互能力。LaMAI利用主动学习技术生成最具信息量的提问，促进动态双向对话。该方法不仅缩小了上下文鸿沟，还能优化LLMs的输出，使其更贴近用户预期。我们在LLMs缺乏对话上下文的多个复杂数据集上开展的实证研究表明了LaMAI的有效性：该方法将答案准确率从31.9%提升至50.9%，优于其他主流问答框架。此外，在涉及人类参与者的场景中，LaMAI在超过82%的案例中生成优于或可媲美基线方法的响应。LaMAI与多种LLMs的成功集成进一步验证了其适用性，凸显了该方案对交互式语言模型未来发展的潜力。