In-Context Learning

Opening — Why this matters now As large language models (LLMs) weave themselves into every enterprise workflow, a quieter issue looms: the privacy of the data used to prompt them. In‑context learning (ICL) — the art of teaching a model through examples in its prompt — is fast, flexible, and dangerously leaky. Each query could expose confidential examples from private datasets. Enter differential privacy (DP), the mathematical armor for sensitive data — except until now, DP methods for ICL have been clumsy and utility‑poor. ...

TL;DR A new synthetic benchmark (INABHYD) tests inductive and abductive reasoning under Occam’s Razor. LLMs handle toy cases but falter as ontologies deepen or when multiple hypotheses are needed. Even when models “explain” observations, they often pick needlessly complex or trivial hypotheses—precisely the opposite of what scientific discovery and root-cause analysis require. The Big Idea Most reasoning work on LLMs obsesses over deduction (step-by-step proofs). But the real world demands induction (generalize rules) and abduction (best explanation). The paper introduces INABHYD, a programmable benchmark that builds fictional ontology trees (concepts, properties, subtype links) and hides some axioms. The model sees an incomplete world + observations, and must propose hypotheses that both explain all observations and do so parsimoniously (Occam’s Razor). The authors score: ...