Despite their impressive performance, large language models (LLMs) such as ChatGPT are known to pose important risks. One such set of risks arises from misplaced confidence, whether over-confidence or under-confidence, that the models have in their inference. While the former is well studied, the latter is not, leading to an asymmetry in understanding the comprehensive risk of the model based on misplaced confidence. In this paper, we address this asymmetry by defining two types of risk (decision and composite risk), and proposing an experimental framework consisting of a two-level inference architecture and appropriate metrics for measuring such risks in both discriminative and generative LLMs. The first level relies on a decision rule that determines whether the underlying language model should abstain from inference. The second level (which applies if the model does not abstain) is the model's inference. Detailed experiments on four natural language commonsense reasoning datasets using both an open-source ensemble-based RoBERTa model and ChatGPT, demonstrate the practical utility of the evaluation framework. For example, our results show that our framework can get an LLM to confidently respond to an extra 20.1% of low-risk inference tasks that other methods might misclassify as high-risk, and skip 19.8% of high-risk tasks, which would have been answered incorrectly.
Evaluating Multimodal Large Language Models with Daily Composite Tasks in Home Environments
State Key Laboratory of General Artificial Intelligence, Beijing Institute for General Artificial Intelligence（通用人工智能国家重点实验室、北京通用人工智能研究院）；School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Key Laboratory of Machine Perception (Ministry of Education), Peking University（心理与认知科学学院及北京行为与心理健康重点实验室、机器感知重点实验室（教育部））；School of Intelligence Science and Technology, Peking University（智能科学与技术学院）
Defining and Evaluating Visual Language Models' Basic Spatial Abilities: A Perspective from Psychometrics
School of Architecture, Tsinghua University（清华大学建筑学院）；Department of Electronic Engineering, Tsinghua University（清华大学电子工程系）；BNRist, Tsinghua University（清华大学BNRist）
