5 papers
Learning Steerable Clarification Policies with Collaborative Self-play
Jonathan Berant, Maximillian Chen, Adam Fisch +4
To handle underspecified or ambiguous queries, AI assistants need a policy for managing their uncertainty to determine (a) when to guess the user intent and answer directly, (b) wh…
Plantain: Plan-Answer Interleaved Reasoning
Anthony Liang, Jonathan Berant, Adam Fisch +3
Reasoning models often spend a significant amount of time thinking before they generate a visible response. In the meantime, they do not give the user any hints as to whether their…
Cost-Optimal Active AI Model Evaluation
Anastasios N. Angelopoulos, Jacob Eisenstein, Jonathan Berant +2
The development lifecycle of generative AI systems requires continual evaluation, data acquisition, and annotation, which is costly in both resources and time. In practice, rapid i…
Don't lie to your friends: Learning what you know from collaborative self-play
Jacob Eisenstein, Reza Aghajani, Adam Fisch +5
To be helpful assistants, AI agents must be aware of their own capabilities and limitations. This includes knowing when to answer from parametric knowledge versus using tools, when…
InfAlign: Inference-aware language model alignment
Ananth Balashankar, Ziteng Sun, Jonathan Berant +9
Language model alignment is a critical step in training modern generative language models. Alignment targets to improve win rate of a sample from the aligned model against the base…