mlsanityData to 14 Oct 2026

A Dialogue Game for Eliciting Balanced Collaboration

arXiv 2406.08202 ↗First posted 12 Jun 2024cs.CLcited by 4

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Abstract (arXiv)

Collaboration is an integral part of human dialogue. Typical task-oriented dialogue games assign asymmetric roles to the participants, which limits their ability to elicit naturalistic role-taking in collaboration and its negotiation. We present a novel and simple online setup that favors balanced collaboration: a two-player 2D object placement game in which the players must negotiate the goal state themselves. We show empirically that human players exhibit a variety of role distributions, and that balanced collaboration improves task performance. We also present an LLM-based baseline agent which demonstrates that automatic playing of our game is an interesting challenge for artificial systems.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 8 of its 31 references that are in the map. First posted on arXiv 12 Jun 2024.

Builds on

8 of 31 references.

References · 8

  1. 2020Open Problems in Cooperative AIcited by 168
  2. 2020Human Instruction-Following with Deep Reinforcement Learning via Transfer-Learning from Textcited by 59
  3. 2019ACTRCE: Augmenting Experience via Teacher's Advice For Multi-Goal Reinforcement Learningcited by 17
  4. 2021Draw Me a Flower: Processing and Grounding Abstraction in Natural Languagecited by 14
  5. 2023What A Situated Language-Using Agent Must be Able to Do: A Top-Down Analysiscited by 8
  6. 2022The slurk Interaction Server Framework: Better Data for Better Dialog Modelscited by 7
  7. 2020Generating Instructions at Different Levels of Abstractioncited by 1
  8. 2024Learning Communication Policies for Different Follower Behaviors in a Collaborative Reference Gamecited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 1.

4 papers.