When Models Know More Than They Can Explain: Quantifying Knowledge
  Transfer in Human-AI Collaboration
Recent advancements in AI reasoning have driven substantial improvements
across diverse tasks. A critical open question is whether these improvements
also yields better knowledge transfer: the ability of models to communicate
reasoning in ways humans can understand, apply, and learn from. To investigate
this, we introduce Knowledge Integration and Transfer Evaluation (KITE), a
conceptual and experimental framework for Human-AI knowledge transfer
capabilities and conduct the first large-scale human study (N=118) explicitly
designed to measure it. In our two-phase setup, humans first ideate with an AI
on problem-solving strategies, then independently implement solutions,
isolating model explanations' influence on human understanding. Our findings
reveal that although model benchmark performance correlates with collaborative
outcomes, this relationship is notably inconsistent, featuring significant
outliers, indicating that knowledge transfer requires dedicated optimization.
Our analysis identifies behavioral and strategic factors mediating successful
knowledge transfer. We release our code, dataset, and evaluation framework to
support future work on communicatively aligned models.
6847057b3ec10bdd8ab4dc8b
Quan Shi
6847057b3ec10bdd8ab4dc8c
Carlos E. Jimenez
6847057b3ec10bdd8ab4dc8d
Shunyu Yao
6847057b3ec10bdd8ab4dc8e
Nick Haber
6847057b3ec10bdd8ab4dc8f
Diyi Yang
6847057b3ec10bdd8ab4dc90
Karthik Narasimhan
When Models Know More Than They Can Explain: Quantifying Knowledge
  Transfer in Human-AI Collaboration
Recent advancements in AI reasoning have driven substantial improvements
across diverse tasks. A critical open question is whether these improvements
also yields better knowledge transfer: the ability of models to communicate
reasoning in ways humans can understand, apply, and learn from. To investigate
this, we introduce Knowledge Integration and Transfer Evaluation (KITE), a
conceptual and experimental framework for Human-AI knowledge transfer
capabilities and conduct the first large-scale human study (N=118) explicitly
designed to measure it. In our two-phase setup, humans first ideate with an AI
on problem-solving strategies, then independently implement solutions,
isolating model explanations' influence on human understanding. Our findings
reveal that although model benchmark performance correlates with collaborative
outcomes, this relationship is notably inconsistent, featuring significant
outliers, indicating that knowledge transfer requires dedicated optimization.
Our analysis identifies behavioral and strategic factors mediating successful
knowledge transfer. We release our code, dataset, and evaluation framework to
support future work on communicatively aligned models.
Research investigates human-AI knowledge transfer through a large-scale study, revealing that AI performance does not consistently correlate with human understanding, requiring dedicated optimization for effective communication.
Knowledge Integration and Transfer Evaluation KITE Human-AI knowledge transfer problem-solving strategies model explanations communicationally aligned models