clarification

This tweet discusses the importance of asking clarifying questions in system design interviews, explaining how questions drive better architecture than rote diagrams.

This paper investigates using LLMs to rewrite fragmentary dialogue utterances for improving frozen discourse parsers, finding that zero-shot clarification is unreliable and that error repair through rewriting has a practical ceiling, suggesting rewritability prediction as a key missing capability.

A tweet highlights that Anthropic conducted large-scale reinforcement learning using Slack conversations, with Andrej Karpathy emphasizing that it is not a trivial Slack bot feature as commonly misinterpreted.

Proposes a goal-oriented clarification framework using Information Gain Reward to train LLM agents to ask effective clarification questions under underspecified user instructions, improving task success rate by 3.7% with minimal interaction overhead.