3 papers
cs.CL2026
From Token Probabilities to Semantic Constraints: Towards Declarative Probabilistic Evaluation of Language Models
Kyle Richardson, Cullen Anderson, Pranav Balakrishnan +4
While Large Language Models have improved rapidly, many fundamental questions remain about how to evaluate the knowledge and reasoning abilities they acquire, and how such evaluati…
cs.CL2026
Probabilistic Programs of Thought
Poorva Garg, Renato Lui Geh, Daniel Israel +3
LLMs are widely used for code generation and mathematical reasoning tasks where they are required to generate structured output. They either need to reason about code, generate cod…
cs.CL2025
A Good Plan is Hard to Find: Aligning Models with Preferences is Misaligned with What Helps Users
Nishant Balepur, Matthew Shu, Yoo Yeon Sung +5
To assist users in complex tasks, LLMs generate plans: step-by-step instructions towards a goal. While alignment methods aim to ensure LLM plans are helpful, they train (RLHF) or e…