Core Concepts
BARDA dataset separates factual accuracy and reasoning ability for evaluation.
Abstract
BARDA dataset aims to distinguish between factual accuracy and reasoning ability in evaluating language models.
The dataset contains 3000 entailments with a mix of true and false statements, including counterfactual examples.
Testing on GPT-series models shows progression in both factual accuracy and reasoning ability.
BARDA offers a new benchmark for evaluating model performance.
Different types of entailments are used to separate factual accuracy from reasoning accuracy.
Metrics like belief accuracy, reasoning accuracy, and consistency are used to evaluate model performance.
Stats
BARDAデータセットには、3000の含意が含まれています。
GPTシリーズモデルのテストでは、事実の正確さと推論能力の進歩が示されました。
モデルスコア(真実)は74.1/80.6/82.6/87.1であり、推論精度スコアは63.1/78.0/71.8/79.2です。