arXiv:2606.04987v1 Announce Type: cross Abstract: Multi-party dialogue is a critical setting for studying collaborative reasoning and decision-making, yet existing datasets rarely focus on structured, in-depth complex reasoning tasks. We introduce DeliChess, a novel dataset of group deliberation dialogues in which participants collaboratively solve multiple-choice chess puzzles. Each group first completes the puzzle individually, then engages in a multi-party discussion before submitting a revised collective answer. The dataset includes 107 dialogues with full transcripts, pre- and post-discus
The continuous growth in AI research necessitates more complex and nuanced datasets to train advanced models, especially in collaborative reasoning and deliberation tasks.
Sophisticated AI systems require datasets that reflect real-world collaborative problem-solving, moving beyond simple Q&A to multi-party deliberation for improved decision-making capabilities.
This dataset offers a new resource for developing AI models capable of understanding, participating in, and potentially facilitating complex group discussions and decision-making processes.
- · AI researchers
- · NLP developers
- · Collaborative AI platforms
- · Chess AI developers
- · AI models trained solely on individual task data
New AI models emerge with enhanced capabilities in multi-party dialogue and collaborative reasoning.
These models could be integrated into tools that assist human groups in problem-solving and decision-making.
Future AI agents might lead or mediate complex human-AI collaborative endeavors, optimizing outcomes in various fields.
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Read at arXiv cs.AI