Tag: #apple-ml13 results
Reasoning trace length serves as simple confidence estimator in LLMs to combat hallucinations. Performs comparably to verbalized confidence across models, datasets, prompts. Post-training alters trace-confidence relationship.
Apple researchers demonstrate that reasoning trace length serves as a simple, effective confidence estimator in large reasoning models. It performs comparably to verbalized confidence across models, datasets, and prompts, acting complementarily. The work shows reasoning post-training alters the trace-confidence relationship.
Study maps UX design space for LLM-based computer use agents via two-phase research. Phase 1 reviewed systems and interviewed eight UX/AI practitioners to create taxonomy. Categories cover user prompts, explainability, user control, and more.