# Any Other Thoughts, Hedgehog? Linking Deliberation Chains in Collaborative Dialogues

arXiv: https://arxiv.org/abs/2410.19301 · HTML: https://mlsanity.com/paper/2410.19301/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 60 references that are in the map.
- First posted on arXiv 25 Oct 2024.

## Builds on (most cited first, 20 of 20)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution](https://mlsanity.com/paper/2202.10054/) (2022)
- [Symbolic Knowledge Distillation: from General Language Models to Commonsense Models](https://mlsanity.com/paper/2110.07178/) (2021)
- [End-to-end Neural Coreference Resolution](https://mlsanity.com/paper/1707.07045/) (2017)
- [Poly-encoders: Transformer Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring](https://mlsanity.com/paper/1905.01969/) (2019)
- [Measuring Association Between Labels and Free-Text Rationales](https://mlsanity.com/paper/2010.12762/) (2020)
- [Question Decomposition Improves the Faithfulness of Model-Generated Reasoning](https://mlsanity.com/paper/2307.11768/) (2023)
- [Cross-document Coreference Resolution over Predicted Mentions](https://mlsanity.com/paper/2106.01210/) (2021)
- [DeliData: A dataset for deliberation in multi-party problem solving](https://mlsanity.com/paper/2108.05271/) (2021)
- [Generalizing Cross-Document Event Coreference Resolution Across Multiple Corpora](https://mlsanity.com/paper/2011.12249/) (2020)
- [Neural Coreference Resolution with Deep Biaffine Attention by Joint Mention Detection and Mention Clustering](https://mlsanity.com/paper/1805.04893/) (2018)
- [Focus on what matters: Applying Discourse Coherence Theory to Cross Document Coreference](https://mlsanity.com/paper/2110.05362/) (2021)
- [2 * n is better than n2: Decomposing Event Coreference Resolution into Two Tractable Problems](https://mlsanity.com/paper/2305.05672/) (2023)
- [Common Ground Tracking in Multimodal Dialogue](https://mlsanity.com/paper/2403.17284/) (2024)
- [What happens before and after: Multi-Event Commonsense in Event Coreference Resolution](https://mlsanity.com/paper/2302.09715/) (2023)
- [Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection](https://mlsanity.com/paper/1906.06703/) (2019)
- [Linear Cross-document Event Coreference Resolution with X-AMR](https://mlsanity.com/paper/2404.08656/) (2024)
- [What makes you change your mind? An empirical investigation in online group decision-making conversations](https://mlsanity.com/paper/2207.12035/) (2022)

## Built on by (most cited first, 6 of 6)

- [Frictional Agent Alignment Framework: Slow Down and Don't Break Things](https://mlsanity.com/paper/2505.19428/) (2025)
- [TRACE: Real-Time Multimodal Common Ground Tracking in Situated Collaborative Dialogues](https://mlsanity.com/paper/2503.09511/) (2025)
- [Learning "Partner-Aware" Collaborators in Multi-Party Collaboration](https://mlsanity.com/paper/2510.22462/) (2025)
- [Dude, where's my utterance? Evaluating the effects of automatic segmentation and transcription on CPS detection](https://mlsanity.com/paper/2507.04454/) (2025)
- [Flout at Your Own Risk: LLMs Struggle with Pragmatic Cooperativity Under Epistemic Asymmetry](https://mlsanity.com/paper/2607.11053/) (2026)
- [Illusion of Alignment: Detecting Hidden Disagreement in Collaborative Dialogue](https://mlsanity.com/paper/2608.08210/) (2026)

## Abstract (arXiv)

Question-asking in collaborative dialogue has long been established as key to knowledge construction, both in internal and collaborative problem solving. In this work, we examine probing questions in collaborative dialogues: questions that explicitly elicit responses from the speaker's interlocutors. Specifically, we focus on modeling the causal relations that lead directly from utterances earlier in the dialogue to the emergence of the probing question. We model these relations using a novel graph-based framework of deliberation chains, and reframe the problem of constructing such chains as a coreference-style clustering problem. Our framework jointly models probing and causal utterances and the links between them, and we evaluate on two challenging collaborative task datasets: the Weights Task and DeliData. Our results demonstrate the effectiveness of our theoretically-grounded approach compared to both baselines and stronger coreference approaches, and establish a standard of performance in this novel task.