We address a not-widely-recognized subset of exploratory search, where a user sets out on a typically long "search quest" for the perfect wedding dress, overlooked research topic, killer company idea, etc. The first few outputs of current large language models (LLMs) may be helpful but only as a start, since the quest requires learning the search space and evaluating many diverse and creative alternatives along the way. Although LLMs encode an impressive fraction of the world's knowledge, common decoding methods are narrowly optimized for prompts with correct answers and thus return mostly homogeneous and conventional results. Other approaches, including those designed to increase diversity across a small set of answers, start to repeat themselves long before search quest users learn enough to make final choices, or offer a uniform type of "creativity" to every user asking similar questions. We develop a novel, easy-to-implement decoding scheme that induces sustained creativity and diversity in LLMs, producing as many conceptually unique results as desired, even without access to the inner workings of an LLM's vector space. The algorithm unlocks an LLM's vast knowledge, both orthodox and heterodox, well beyond modal decoding paths. With this approach, search quest users can more quickly explore the search space and find satisfying answers.

翻译：我们针对一种尚未被广泛认知的探索性搜索子类问题展开研究：用户通常需要进行漫长的“搜索探索”（如寻找完美婚纱、未被充分研究的研究课题、颠覆性的公司创意等）。当前大型语言模型（LLMs）的前几个输出结果虽有一定帮助，但仅能作为起点，因为此类探索需要用户在学习搜索空间的同时，沿途评估大量多样且富有创造性的替代方案。尽管LLMs编码了人类知识库中相当可观的内容，但常见的解码方法主要针对具有标准答案的提示进行优化，导致输出结果趋于同质化和常规化。其他方法（包括旨在提升小规模备选答案多样性的方案）在搜索探索用户尚未充分获取决策所需信息时便出现重复，或对不同用户提出的相似问题提供千篇一律的“创意”。我们开发了一种新颖且易于实现的解码方案，能够激发LLMs的持续创造力与多样性，可生成任意数量的概念性独特结果，即使无法访问LLM向量空间的内部机制也依然有效。该算法突破了模态解码路径的局限，充分释放LLMs蕴含的正统与非正统知识。通过此方法，搜索探索用户可更快速地探索搜索空间并找到满意的答案。