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：我们探讨了探索性搜索中一个尚未被广泛认识的子类，即用户开启一段通常较长的“搜索征途”，寻找完美的婚纱、被忽视的研究课题、绝佳的公司创意等。当前大型语言模型（LLMs）生成的前几项输出或许有所助益，但仅能作为起点，因为该征途需要学习搜索空间，并在过程中评估多种多样且富有创意的备选方案。尽管LLMs编码了世界知识的可观部分，但常见的解码方法却狭隘地针对正确答案的提示进行优化，因此返回的结果大多同质化且常规。其他方法，包括那些旨在增强小规模答案集多样性的方法，会在搜索征途用户学习足够知识以做出最终选择之前便开始重复输出，或为提问相似问题的每位用户提供类型统一的“创造性”。我们开发了一种新颖且易于实现的解码方案，可在LLMs中激发持续创造力与多样性，按需生成任意数量的概念独特结果，甚至无需访问LLMs向量空间的内部机制。该算法解锁了LLMs的广阔知识库，涵盖正统与异端观点，远超常规解码路径。借助这一方法，搜索征途用户能更快速地探索搜索空间并找到满意的答案。