Do language models possess comprehensive understanding of ordinary concepts? A study published in ACL2023.
Introduction:
Language models, such as GPT-3 and Macaw, have been widely used for various natural language processing tasks. However, their understanding of everyday objects and their internal representations, known as mental models, have not been extensively studied. In our paper, “Do language models have coherent mental models of everyday things?”, we investigate whether language models have coherent mental models of common objects we encounter in our daily lives.
Mental models are essential for human reasoning and understanding of how things work. For instance, when making a fried egg, we know that the shell surrounds the egg white and yolk. Without a coherent mental model, a system might have inaccurate and illogical representations, leading to ineffective interactions with objects.
To explore this, we introduce the ParRoT (Parts and Relations of Things) dataset, which contains 300 mental models of 100 everyday objects. We asked human subjects to sketch the mental models in the form of graphs, where each node represents a part of the object, and each edge represents a relationship between parts.
We evaluate the mental models of language models by querying them with true or false statements about object relationships. Surprisingly, our results show that state-of-the-art language models perform poorly in generating coherent mental models, with a significant percentage of violations and low accuracy compared to human mental models.
To address this limitation, we propose ParRoT-Con, a neuro-symbolic method that combines a language model with a reasoning layer to enhance the accuracy and coherence of mental models. By applying constraint reasoning on raw language model predictions, we improve the consistency and accuracy of mental models by 16-20%.
Our work not only highlights the limitations of language models in understanding everyday objects but also provides a benchmark dataset and methodology for researchers to study mental models of various objects. We also encourage the development of improved cognitive architectures that combine language models with reasoning layers for better mental model construction.
In conclusion, our study emphasizes the importance of coherent mental models in language models’ understanding and interactions with everyday objects. By addressing the shortcomings and enhancing the mental models, we can improve the capabilities of language models in diverse applications.
Full Article: Do language models possess comprehensive understanding of ordinary concepts? A study published in ACL2023.
Language models have become increasingly sophisticated in recent years, but do they possess coherent mental models of everyday objects and concepts? This is the question explored in a recent paper presented at ACL 2023. The concept of mental models, proposed by Kenneth Craik in 1943, suggests that thinking involves the manipulation of internal representations of the world. Coherent mental models are fundamental to human reasoning, enabling us to understand how things work and how to interact with them.
The paper focuses specifically on mental models related to everyday objects and their spatial relationships. For example, when someone thinks about a fried egg, they know that it has a shell, egg white, and yolk. However, if a system lacks a coherent mental model of an egg, it may mistakenly believe that the yolk surrounds the shell. This can lead to nonsensical approaches, such as attempting to scrape the yolk off the shell.
To investigate whether language models (LMs) have coherent mental models of everyday things, the researchers created a benchmark dataset called ParRoT (Parts and Relations of Things). They asked human subjects to sketch a mental model for each everyday object, representing the object as a graph. In these graphs, each node represents a part of the object, and each edge represents a relationship between two parts. For example, for an egg, the graph would show that the shell surrounds the egg white, and the egg white surrounds the yolk.
Using these annotations, the researchers constructed the ParRoT dataset, which consists of 300 mental models across 100 everyday objects. They then queried different LMs, such as GPT-3 and Macaw, with True/False statements about the objects’ spatial relationships. Based on the LM’s responses, they assembled the LM’s mental model of each object.
The results showed that the mental models derived from the LMs’ predictions were significantly inconsistent, with 19-43% conditional violation compared to the gold mental models in the ParRoT dataset. The accuracy of the LMs’ predictions ranged from 54-59%, just slightly better than the majority class baseline of 59% and random chance at 50%.
To address these inconsistencies, the researchers proposed a neuro-symbolic method called ParRoT-Con. This method combines a language model with a reasoning layer that applies commonsense constraints to the LM’s raw predictions. This constraint reasoning helps create a more coherent mental picture of the object.
Not only did ParRoT-Con remove inconsistencies, but it also significantly improved the accuracy of the mental models by 16-20%. This suggests that combining language models with reasoning layers can lead to more accurate and consistent mental models of everyday things.
The researchers believe that their work has important implications for the field. The ParRoT dataset provides a valuable resource for studying mental models of everyday objects and their relationships. Moreover, the ParRoT-Con method suggests a broader cognitive architecture for future systems, combining language models with reasoning layers to construct more accurate and coherent mental models.
The researchers also highlight several future research directions. They suggest using the ParRoT dataset to study how humans’ mental models of everyday things evolve over time. Additionally, they propose exploring the impact of more coherent mental models on complex reasoning tasks about everyday things and integrating mental models of different dimensions, such as time and space.
In conclusion, this research explores the coherence of mental models in language models of everyday things. The ParRoT dataset and the ParRoT-Con method contribute to our understanding of mental models and provide a foundation for further research in this area. By improving the coherence and accuracy of mental models, language models can become more effective in representing and reasoning about everyday objects and concepts.
Summary: Do language models possess comprehensive understanding of ordinary concepts? A study published in ACL2023.
In a recent study, researchers investigated whether language models (LMs) possess coherent mental models of everyday objects. Mental models, which are internal representations of the world, are crucial for human reasoning. The researchers created a benchmark dataset called ParRoT (Parts and Relations of Things) that contains 300 mental models across 100 everyday objects. They also developed a neuro-symbolic method called ParRoT-Con to improve the accuracy and consistency of LMs’ mental models. The results showed that LMs’ mental models were significantly inconsistent compared to human mental models. However, the ParRoT-Con method improved the accuracy and consistency of LMs’ mental models. This study highlights the importance of understanding LMs’ mental models and suggests a cognitive architecture combining LM and reasoning for future systems.
