CHIRON: Complementing Human Intelligence to Recognize Opponent Narratives

Project funded by DARPA
PI: Jonathan May

The Friction for Accountability in Conversational Transactions (FACT) Artificial Intelligence Exploration (AIE) opportunity will explore human-AI dialogue-based methods that avoid over-trust through reflective reasoning (“friction”) that reveals implicit assumptions between dialogue partners, enabling accountable decision-making in complex environments. FACT aims to develop and evaluate human-AI conversation-shaping algorithms that 1) capture mutual assumptions, views, and intentions based on dialogue history, 2) auto-assess the consequences of potential actions and the level of accountability for responses, and 3) reveal implicit costs and assumptions to the user, prompting critical analysis, and proposing course changes as appropriate.

	Jordan Boyd-Graber Associate Professor, Computer Science (UMD)
	Jonathan Kummerfeld PI, Sydney
	Jonathan May PI, USC ISI

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• Chenglei Si, Navita Goyal, Tongshuang Wu, Chen Zhao, Shi Feng, Hal Daumé III, and Jordan Lee Boyd-Graber. Large Language Models Help Humans Verify Truthfulness---Except When They Are Convincingly Wrong. North American Association for Computational Linguistics, 2024. [Bibtex]
  
  

  @inproceedings{Si:Goyal:Wu:Zhao:Feng:III:Boyd-Graber-2024,
  	Title = {Large Language Models Help Humans Verify Truthfulness---Except When They Are Convincingly Wrong},
  	Author = {Chenglei Si and Navita Goyal and Tongshuang Wu and Chen Zhao and Shi Feng and Hal Daum\'{e} {III} and Jordan Lee Boyd-Graber},
  	Booktitle = {North American Association for Computational Linguistics},
  	Year = {2024},
  	Url = {http://umiacs.umd.edu/~jbg//docs/2024_naacl_convincingly.pdf},
  }
  

Acknowledgments

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the researchers and do not necessarily reflect the views of the sponsor.