11 papers
CollabEval: Statistically Efficient Collaborative Model Evaluation via Matrix Completion
Adam Fisch, Daniel Deutsch, Joshua Maynez +5
Evaluating generative AI models is a routine, but resource-intensive, process that is conducted over and over again during the course of model development. In this work, we propose…
Robust LLM Performance Certification via Constrained Maximum Likelihood Estimation
Minghe Shen, Ananth Balashankar, Adam Fisch +2
The ability to rigorously estimate the failure rates of large language models (LLMs) is a prerequisite for their safe deployment. Currently, however, practitioners often face a tra…
Rich Insights from Cheap Signals: Efficient Evaluations via Tensor Factorization
Felipe Maia Polo, Aida Nematzadeh, Virginia Aglietti +2
Moving beyond evaluations that collapse performance across heterogeneous prompts toward fine-grained evaluation at the prompt level, or within relatively homogeneous subsets, is ne…
MT-PingEval: Evaluating Multi-Turn Collaboration with Private Information Games
Jacob Eisenstein, Fantine Huot, Adam Fisch +2
We present a scalable and verifiable methodology for evaluating language models in multi-turn interactions, using a suite of collaborative games that require effective communicatio…
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…