Standards for Belief Representations in LLMs (2405.21030v2)

Abstract: As 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.

• The paper proposes four core criteria—accuracy, coherence, uniformity, and use—to identify belief-like representations in LLMs.
• It integrates philosophical insights with machine learning techniques to address the challenge of mapping internal vector states to cognitive capacities.
• It emphasizes the role of probes and empirical tests to validate whether these internal representations effectively guide LLM outputs.

Evaluating Criteria for Belief Representation in LLMs

The paper "Standards for Belief Representations in LLMs" by Herrmann and Levinstein examines the understudied challenge of discerning belief-like representations within LLMs. With the transformative capabilities LLMs have demonstrated in generating coherent text, the question of whether these models hold and utilize representations akin to beliefs becomes salient. The authors tackle this conceptual and empirical dilemma, proposing a set of conditions under which belief can be meaningfully attributed to LLMs.

Context and Motivation

LLMs, primarily built on transformer architectures, have achieved significant fluency and usability in text generation. Nevertheless, a thorough understanding of their internal representational mechanisms remains elusive. Prevailing research focuses on whether LLMs possess a form of cognitive capacity resembling belief, typically a combination of internal state representation and appropriative output. The authors argue the importance of clarifying such capacities, informed by insights from decision theory and formal epistemology.

The paper takes an interdisciplinary approach, integrating philosophical reflections on belief with contemporary machine learning practices. This involves conceptual and methodological aspects, addressing how belief, if present, might be measured and analyzed within LLMs.

Proposed Criteria for Belief-Like Representation

The authors establish four core conditions that candidate belief representations should meet within LLMs: accuracy, coherence, uniformity, and use. Each serves a distinct purpose:

1. Accuracy: This criterion requires that the belief representations map onto true states of the world, at least with a high degree of probability. The authors emphasize that true beliefs should underpin an LLM's effective performance. Given the specificity of training and testing datasets, where confirmation of truth is unequivocal, accuracy is pivotal.
2. Coherence: The paper argues that for a representation to be belief-like, it must exhibit logical consistency and integration across various statements and contexts. Coherence encompasses both probabilistic and logical compatibilities, underscoring belief content richness and consistency over different expression forms.
3. Uniformity: Belief representations should remain consistent across diverse domains—demonstrating similar structural and content-related fidelity irrespective of subject matter. This criterion strives for a single, coherent way in which LLMs could internally encode truth, offering an informative unity across different domains.
4. Use: Perhaps the most operationally intricate, this condition posits that any identified belief representation must be shown to guide the LLM’s behaviors and outputs. Intervention tests and manipulations are proposed to confirm whether the internal representations causally impact the model's outputs.

The Role of Probes

The researchers emphasize the utility of probes—external models used to interpret hidden states within LLMs. These tools attempt to draw relationships between internal vector representations and belief-like stances, using coherence and use tests to elucidate whether the captured representations fulfill the authors' proposed criteria. Such probes act as investigative instruments to determine if, and how, belief representations within LLMs are operationalized.

Empirical Considerations and Future Directions

The paper acknowledges the fragile nature of current belief elicitation attempts, highlighting how existing approaches often fail to capture nuanced degrees of coherence and use. The authors discuss previous empirical attempts, pointing out their limitations, notably the lack of consistency when negations or semantic variants are introduced in testing data. They advocate a robust empirical methodology to unveil a cohesive blueprint for uncovering belief-like representations in LLMs.

Herrmann and Levinstein's proposed framework aligns philosophically informed criteria with practical machine learning considerations. The forward-oriented discourse suggests potential pathways for verifying belief-like structures within LLMs. Successfully doing so would not only enhance interpretability but also support the responsible integration of LLMs into human-centric applications, ensuring ethical utilization and behavior predictability.

The paper advocates for an interdisciplinary research trajectory to further refine these criteria and address their intersections with ethical AI implementation. As research progresses, understanding belief representations could offer significant insights into LLM cognition and align better with the technological horizon that AI presents.