semantic-entropy-2e7411c7·2 events·first seen Aliases: Semantic Entropy
Researchers probe four Polish Bielik LLMs (1.5B–11B parameters) to test whether internal activations reveal entity familiarity before any answer token is generated. Using unsupervised dispersion measures over post-SwiGLU MLP activations, they achieve AUROC 0.95–1.00 in separating known from fabricated entities—a signal that is already at ceiling at 1.5B parameters. Despite this internal awareness, the models almost never abstain: only 2 refusals and 1 hedge were found across 2,520 answers. The paper distinguishes entity familiarity (a representational phenomenon stable across scale) from factual reliability (which scales sharply with model size), with implications for hallucination detection and calibration research.
Researchers introduce operadic consistency (OC), a label-free inference-time signal that checks whether an LLM's direct answer to a compositional query agrees with the answer produced by composing its own stated decomposition of that query. Evaluated across 12 instruction-tuned LLMs (4B–671B parameters) on four multi-hop QA datasets, OC achieves Pearson r ∈ [0.86, 0.94] with accuracy uniformly across all datasets, outperforming self-consistency, semantic entropy, and P(True) in cross-dataset robustness. At the per-question level, OC provides information beyond existing baselines and yields selective-prediction improvements (AUARC lifts +0.086–0.096, AUROC lifts +0.092–0.164) at equal sampling cost, with results extending to frontier thinking models using chain-of-thought decompositions.