This paper investigates the potential of AI models, particularly large
language models (LLMs), to support knowledge exploration and augment human
creativity during ideation. We present "Latent Lab" an interactive tool for
discovering connections among MIT Media Lab research projects, emphasizing
"exploration" over search. The work offers insights into collaborative AI
systems by addressing the challenges of organizing, searching, and synthesizing
content. In a user study, the tool's success was evaluated based on its ability
to introduce users to an unfamiliar knowledge base, ultimately setting the
groundwork for the ongoing advancement of human-AI knowledge exploration
systems.

本文研究人工智能模型（特别是大型语言模型）在知识探索和创意增强过程中的潜力。我们展示了一个名为 “潜在实验室” 的交互工具，用于发现麻省理工媒体实验室研究项目之间的联系，强调 “探索” 而非搜索。通过解决组织、搜索和综合内容的挑战，这项工作为协作式人工智能系统提供了深入见解。在一项用户研究中，根据该工具引入用户到一个陌生的知识库的能力来评估其成功，最终为人工智能知识探索系统的不断进步奠定了基础。

潜在实验室：用于知识探索的大型语言模型

Latent Lab: Large Language Models for Knowledge Exploration

Open domain conversational agents can answer a broad range of targeted
queries. However, the sequential nature of interaction with these systems makes
knowledge exploration a lengthy task which burdens the user with asking a chain
of well phrased questions. In this paper, we present a retrieval based system
and associated dataset for predicting the next questions that the user might
have. Such a system can proactively assist users in knowledge exploration
leading to a more engaging dialog. The retrieval system is trained on a dataset
which contains ~14K multi-turn information-seeking conversations with a valid
follow-up question and a set of invalid candidates. The invalid candidates are
generated to simulate various syntactic and semantic confounders such as
paraphrases, partial entity match, irrelevant entity, and ASR errors. We use
confounder specific techniques to simulate these negative examples on the
OR-QuAC dataset and develop a dataset called the Follow-up Query Bank
(FQ-Bank). Then, we train ranking models on FQ-Bank and present results
comparing supervised and unsupervised approaches. The results suggest that we
can retrieve the valid follow-ups by ranking them in higher positions compared
to confounders, but further knowledge grounding can improve ranking
performance.

本文提出了一种基于检索的系统和相关数据集，用于预测用户可能有的下一个问题，可以主动地帮助用户进行知识探索，引导用户进行更有意义的对话。该检索系统是在一个包含 14000 多组信息获取对话的数据集上进行训练的，该数据集包含一个有效的后继问题和一组无效候选问题。我们在 Follow-up Query Bank 数据集上训练了排名模型，并比较了监督和无监督方法的结果。结果表明，我们可以通过将有效后继问题排在更高的位置上进行检索，但进一步的知识构建可以提高排名性能。

学习检索有吸引力的后续查询

Learning to Retrieve Engaging Follow-Up Queries

This paper describes RESOLVE, a system that uses decision trees to learn how
to classify coreferent phrases in the domain of business joint ventures. An
experiment is presented in which the performance of RESOLVE is compared to the
performance of a manually engineered set of rules for the same task. The
results show that decision trees achieve higher performance than the rules in
two of three evaluation metrics developed for the coreference task. In addition
to achieving better performance than the rules, RESOLVE provides a framework
that facilitates the exploration of the types of knowledge that are useful for
solving the coreference problem.

该论文介绍了一个名为 RESOLVE 的系统，该系统利用决策树来学习如何对业务合资企业领域中的同指短语进行分类，实验结果表明，决策树在两个核心问题评估指标方面比规则实现的性能更高，并提供了一种框架，可以便于探索解决同指问题所需的类型的知识。