Conversation demands attention. Speakers must call words to mind, listeners
must make sense of them, and both together must negotiate this flow of
information, all in fractions of a second. We used large language models to
study how this works in a large-scale dataset of English-language conversation,
the CANDOR corpus. We provide a new estimate of the information density of
unstructured conversation, of approximately 13 bits/second, and find
significant effects associated with the cognitive load of both retrieving, and
presenting, that information. We also reveal a role for backchannels -- the
brief yeahs, uh-huhs, and mhmms that listeners provide -- in regulating the
production of novelty: the lead-up to a backchannel is associated with
declining information rate, while speech downstream rebounds to previous rates.
Our results provide new insights into long-standing theories of how we respond
to fluctuating demands on cognitive resources, and how we negotiate those
demands in partnership with others.

使用大型语言模型研究英语对话的大规模数据集，提供了关于非结构化对话信息密度的新估计，以及涉及检索和呈现信息的认知负荷的显著效应，同时揭示了后导评价词在调节新颖性产生中的作用。我们的研究结果为我们如何应对认知资源波动的长期理论和与他人合作应对这些需求的方式提供了新的见解。

从 “嗯” 到 “是的”：人类对话中的信息生成、预测和调控

From "um" to "yeah": Producing, predicting, and regulating information  flow in human conversation

Questions combine our mastery of language with our remarkable facility for
reasoning about uncertainty. How do people navigate vast hypothesis spaces to
pose informative questions given limited cognitive resources? We study these
tradeoffs in a classic grounded question-asking task based on the board game
Battleship. Our language-informed program sampling (LIPS) model uses large
language models (LLMs) to generate natural language questions, translate them
into symbolic programs, and evaluate their expected information gain. We find
that with a surprisingly modest resource budget, this simple Monte Carlo
optimization strategy yields informative questions that mirror human
performance across varied Battleship board scenarios. In contrast, LLM-only
baselines struggle to ground questions in the board state; notably, GPT-4V
provides no improvement over non-visual baselines. Our results illustrate how
Bayesian models of question-asking can leverage the statistics of language to
capture human priors, while highlighting some shortcomings of pure LLMs as
grounded reasoners.

使用大型语言模型生成自然语言问题，将其转化为符号程序，并评估其预期信息增益，从而在有限的认知资源下提出信息丰富的问题。结果表明，这种简单的蒙特卡洛优化策略可以在各种战舰游戏场景中产生与人类表现相似的有信息的问题，而纯语言模型则在将问题与游戏状态联系起来方面遇到一些困难。

战舰开火：自然语言指导下的采样式程序提问

Loose LIPS Sink Ships: Asking Questions in Battleship with  Language-Informed Program Sampling

How are people able to plan so efficiently despite limited cognitive
resources? We aimed to answer this question by extending an existing model of
human task decomposition that can explain a wide range of simple planning
problems by adding structure information to the task to facilitate planning in
more complex tasks. The extended model was then applied to a more complex
planning domain of spatial navigation. Our results suggest that our framework
can correctly predict the navigation strategies of the majority of the
participants in an online experiment.

人们如何能够在有限的认知资源下高效地进行规划？通过对一个现有模型的扩展来回答这个问题，我们通过在任务中添加结构信息以便在更复杂的任务中促进规划，对人类任务分解的模型进行了扩展。扩展模型随后被应用于一个更复杂的空间导航规划领域，我们的结果表明我们的框架可以正确预测绝大多数在线实验参与者的导航策略。