Reasoning Chains Boost Fact Recall in LLMs, Revealing Hidden Knowledge
Google researchers show that letting LLMs generate a brief reasoning trace lets them pull correct answers that are otherwise invisible in the model’s top‑1 output. Two mechanisms—latent computation buffering and factual priming—explain the effect, suggesting that simple chain‑of‑thought prompting can substantially improve closed‑book QA without extra training.