Standards for Belief Representations in LLMs

Authors: Daniel A. Herrmann, Benjamin A. Levinstein

Year: 2024

Source: https://arxiv.org/abs/2405.21030

TLDR:

The document discusses the need for a unified theoretical foundation to understand how large language models (LLMs) represent beliefs about the world. It proposes four criteria for a representation in an LLM to count as belief-like: accuracy, coherence, uniformity, and use. These criteria aim to balance theoretical considerations with practical constraints and provide a comprehensive understanding of belief representation in LLMs. The document also highlights the challenges and limitations of using individual criteria in isolation to identify belief representations. Additionally, it addresses the difficulties in testing for use in beliefs, the limitations of accuracy alone as a method for identifying beliefs, and the importance of coherence in measuring belief-like representations. The work also emphasizes the need for a shared understanding of the overall goal and theoretical basis in the study of belief in LLMs. It discusses the challenges in interpreting the minds of LLMs and the advantages of having low-level access to their internal workings. The document also touches on the limitations of standard methods for eliciting beliefs from LLMs and the need for comprehensive tests across various domains and tasks. Overall, the document provides a comprehensive overview of the challenges and considerations in understanding belief representation in LLMs.

Free Login To Access AI Capability

Free Access To ChatGPT

The document proposes adequacy conditions for belief-like representations in large language models (LLMs), introducing four criteria - accuracy, coherence, uniformity, and use - to provide a theoretical foundation for understanding belief representation in LLMs.

Free Access to ChatGPT

Abstract

As large language models (LLMs) continue to demonstrate remarkable abilities across various domains, computer scientists are developing methods to understand their cognitive processes, particularly concerning how (and if) LLMs internally represent their beliefs about the world. However, this field currently lacks a unified theoretical foundation to underpin the study of belief in LLMs. This article begins filling this gap by proposing adequacy conditions for a representation in an LLM to count as belief-like. We argue that, while the project of belief measurement in LLMs shares striking features with belief measurement as carried out in decision theory and formal epistemology, it also differs in ways that should change how we measure belief. Thus, drawing from insights in philosophy and contemporary practices of machine learning, we establish four criteria that balance theoretical considerations with practical constraints. Our proposed criteria include accuracy, coherence, uniformity, and use, which together help lay the groundwork for a comprehensive understanding of belief representation in LLMs. We draw on empirical work showing the limitations of using various criteria in isolation to identify belief representations.

Method

The authors proposed adequacy conditions for belief-like representations in large language models (LLMs) by drawing from insights in philosophy and contemporary practices of machine learning. They established four criteria - accuracy, coherence, uniformity, and use - to provide a theoretical foundation for understanding belief representation in LLMs. The methodology involved a balance of theoretical considerations with practical constraints, aiming to lay the groundwork for a comprehensive understanding of belief representation in LLMs. Additionally, the authors highlighted the limitations of using various criteria in isolation to identify belief representations and emphasized the need for a unified theoretical foundation in the study of belief in LLMs.

Main Finding

The authors proposed adequacy conditions for a representation in a large language model (LLM) to be considered belief-like, drawing from insights in philosophy and contemporary practices of machine learning. They established four criteria - accuracy, coherence, uniformity, and use - to provide a theoretical foundation for understanding belief representation in LLMs, aiming to balance theoretical considerations with practical constraints and lay the groundwork for a comprehensive understanding of belief representation in LLMs. They also highlighted the limitations of using various criteria in isolation to identify belief representations and emphasized the need for a unified theoretical foundation in the study of belief in LLMs. The authors' discoveries also included the challenges and considerations in understanding belief representation in LLMs, as well as the social and ethical implications of discovering internal representations of truth in LLMs.

Conclusion

The conclusion of this paper is the proposal of adequacy conditions for a representation in a large language model (LLM) to be considered belief-like, drawing from insights in philosophy and contemporary practices of machine learning. The authors establish four criteria - accuracy, coherence, uniformity, and use - to provide a theoretical foundation for understanding belief representation in LLMs, aiming to balance theoretical considerations with practical constraints and lay the groundwork for a comprehensive understanding of belief representation in LLMs. They also highlight the limitations of using various criteria in isolation to identify belief representations and emphasize the need for a unified theoretical foundation in the study of belief in LLMs.

Keywords

large language models, LLMs, belief representation, adequacy conditions, accuracy, coherence, uniformity, use, decision theory, formal epistemology, philosophy, machine learning, cognitive processes, empirical work, truth, falsehood, probes, neural networks, interpretability, theoretical foundation, semantic coherence, measurement techniques, Boolean combinations, machine learning practice.

The Best AI PDF Reader