Steven-Shine Chen

prompt optimization + context distillation are underexplored primitives for post-training pipelines imo

Announcing the ARC Prize 2025 Top Score & Paper Award winners The Grand Prize remains unclaimed Our analysis on AGI progress marking 2025 the year of the refinement loop

OpenAI realesed new paper. "Why language models hallucinate" Simple ans - LLMs hallucinate because training and evaluation reward guessing instead of admitting uncertainty. The paper puts this on a statistical footing with simple, test-like incentives that reward confident wrong answers over honest “I don’t know” responses. The fix is to grade differently, give credit for appropriate uncertainty and penalize confident errors more than abstentions, so models stop being optimized for blind guessing. OpenAI is showing that 52% abstention gives substantially fewer wrong answers than 1% abstention, proving that letting a model admit uncertainty reduces hallucinations even if accuracy looks lower. Abstention means the model refuses to answer when it is unsure and simply says something like “I don’t know” instead of making up a guess. Hallucinations drop because most wrong answers come from bad guesses. If the model abstains instead of guessing, it produces fewer false answers. 🧵 Read on 👇