UNPACKING LARGE LANGUAGE MODELS WITH CON-CEPTUAL CONSISTENCY

Abstract

If a Large Language Model (LLM) answers "yes" to the question "Are mountains tall?" then does it know what a mountain is? Can you rely on it responding correctly or incorrectly to other questions about mountains? The success of Large Language Models (LLMs) indicates they are increasingly able to answer queries like these accurately, but that ability does not necessarily imply a general understanding of concepts relevant to the anchor query. We propose conceptual consistency to measure a LLM's understanding of relevant concepts. This novel metric measures how well a model can be characterized by finding out how consistent its responses to queries about conceptually relevant background knowledge are. To compute it we extract background knowledge by traversing paths between concepts in a knowledge base and then try to predict the model's response to the anchor query from the background knowledge. We investigate the performance of current LLMs in a commonsense reasoning setting using the CSQA dataset and the ConceptNet knowledge base. While conceptual consistency, like other metrics, does increase with the scale of the LLM used, we find that popular models do not necessarily have high conceptual consistency. Our analysis also shows significant variation in conceptual consistency across different kinds of relations, concepts, and prompts. This serves as a step toward building models that humans can apply a theory of mind to, and thus interact with intuitively.

1. INTRODUCTION

Large Language Models (LLMs) have had many exciting recent successes. These include high performance and even emergent capabilities using just zero or few-shot prompting (Brown et al., 2020; Wei et al., 2022a) , but overall performance is still low compared to humans on a wide range of tasks for even the largest models (Srivastava et al., 2022) , and our understanding of these models work is still limited. A popular explanation of low performance and inconsistencies is that LLMs are simply learning to mimic the data used to train them, and this basic pattern recognition limits generalizability, in the case of LLMs exposing the limits of any understanding (Zhang et al., 2022a; Bender & Koller, 2020) . We would use a similar line of reasoning to guess how a LLM would answer the following question: "Can GPT-3 see?" If it performed well on examples from the same distribution we would say it is likely to get it right or vice-versa if performed poorly on those examples. Though valid, this explanation is incomplete because it is completely agnostic to the specific content of the statement. We would apply the exact same reasoning and come to the same conclusion for similar statements about say blood banks or mock trials, as long as they were from the same distribution (in this example, the CSQA2 dataset (Talmor et al., 2021) ). This is in contrast to our day to day life, where our Theory of Mind allows us to understand other agents (people) by attributing beliefs, intentions, and desires to them (Premack & Woodruff, 1978) in a way that allows us to usefully predict their behavior (Rabinowitz et al., 2018; Dennett, 1991) . Beliefs are most relevant here, and should be conceptual in order to best support human understanding (Yeh et al., 2021) . Ideally we would also be able to apply this kind of understanding to LLMs, predicting that the model is more likely correct about GPT-3's sight if it knows about GPT-3 generally than if it does not. This would be a conceptual model of the LLM that allows us to predict its behavior.

