Introduction
The ability to ask effective and informative questions is a peculiar feature of human intelligence. From an evolutionary perspective, it has been argued that being able to ask questions to reliable informants gives humans a key evolutionary advantage over other species (Tomasello, 2009 ) . Questions are also shown to play a fundamental role in cognitive development and language learning during childhood (Demetras et al., 1986 ; Chouinard et al., 2007 ; Leech et al., 2013 ; Ruggeri et al., 2016 , 2017 ) . Both children and adults generally ask questions to resolve a state of uncertainty (Piaget, 1954 ; Rothe et al., 2018 ) .
Deciding how to resolve uncertainties and maintaining mutual understanding are essential steps to achieve conversational grounding (Clark and Schaefer, 1987 ; Clark and Brennan, 1991 ) , the process through which humans negotiate the production and acceptance of their utterances. Clarification questions are among the key strategies speakers use to achieve conversational grounding. However, while humans handle and resolve uncertainties through clarification questions almost effortlessly in everyday conversations, this is particularly challenging for modern dialogue systems based on deep neural networks (Chandu et al., 2021 ) . Even the new generation of conversational agents, such as ChatGPT, 1 1 1 https://chat.openai.com/ seem to struggle to generate clarification questions when dealing with ambiguous instructions (Deng et al., 2023 ) .

In this paper, we investigate uncertainty-driven strategies for asking clarification questions. Clarification requests are usually triggered when ambiguity and/or underspecification make it difficult for the receiver to follow up with enough confidence (Purver, 2004 ; Benotti and Blackburn, 2017 ; Pezzelle and Fernández, 2023 ) . It should come as no surprise then that there is a long-standing tradition of work highlighting the importance of incorporating clarification requests in dialogue systems (Paek and Horvitz, 2000 ; Gabsdil, 2003 ; Schlangen, 2004 ; Rieser and Moore, 2005 ; Rieser and Lemon, 2006 , among others) .
Recent years have witnessed a renewed interest in this ambitious goal, especially in the context of collaborative dialogue tasks (e.g., Nguyen and Daumé III, 2019 ; Chi et al., 2020 ; Zhu et al., 2021 ; Shi et al., 2022 ; Shen and Lourentzou, 2023 ; Madureira and Schlangen, 2023b ) . Yet, several key aspects remain under-explored.

For example, some existing approaches use a dedicated neural network model trained on human clarification behaviour to decide when to ask (Shi et al., 2022 ; Madureira and Schlangen, 2023b ) . However, it is well known that humans may follow different confirmation-seeking strategies (Schegloff et al., 1977 ; Schegloff, 1987 ; Purver et al., 2001 ) . This potential human disagreement casts doubt on the effectiveness of using human decisions as training signal for supervised or reinforcement learning. As an alternative approach, model uncertainty has often been employed to decide when and/or what to ask (Nguyen and Daumé III, 2019 ; Zhu et al., 2021 ; Shen and Lourentzou, 2023 ; Chi et al., 2020 ) . Yet the two types of approaches have not been directly compared, nor is there any study (to the best of our knowledge) on the extent to which model uncertainty correlates with human uncertainty. In our work, we take a closer look at these fundamental aspects.

We consider the CoDraw task and dataset (Kim et al., 2019 ) —where a Drawer player is asked to recreate a clipart scene following instructions from a Teller player—as a testbed to study uncertainty-driven clarification strategies in collaborative agents. We propose different ways to represent model uncertainty in CoDraw, and then carry out a study on the relation between model uncertainty and human uncertainty, as measured by the decision to ask clarification requests in human-human dialogues.
This study reveals that there is a poor relation between model and human uncertainty, which in part appears to stem from our finding that in CoDraw human players do not follow a consistent clarification strategy.
This poor relation suggests that using human clarification decisions as supervision for deciding when to ask may not be the most effective way to resolve model uncertainty.
We therefore propose a new Drawer agent model equipped with an uncertainty-based clarification module (illustrated in Figure 1 ), where we use a template-based approach to generate clarification questions triggered by the uncertainty state of the agent. This approach allows us to modulate the level of uncertainty that leads to posing a question and thus control for the number of questions asked per dialogue, which may be advantageous given the processing cost of questions for humans as well as models (Chiyah-Garcia et al., 2023 ) .

Our results and analyses show that our proposed model is significantly more effective in terms of task success than several strong baselines, including a version based on the human supervision model by Madureira and Schlangen ( 2023b ) . Overall, our findings highlight the importance of equipping conversational agents with the ability to assess and act upon their own uncertainty by asking effective clarification questions.