Model Performance Comparison

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About the Study: Belief in the Machine

Research Context and Importance

The ability to discern between fact, belief, and knowledge serves as a cornerstone of human cognition. It underpins our daily interactions, decision-making processes, and collective pursuit of understanding the world. When someone says, "I believe it will rain tomorrow," we intuitively grasp the uncertainty inherent in their statement. Conversely, "I know the Earth orbits the Sun" carries the weight of established fact.

As artificial intelligence (AI), particularly large language models (LMs), becomes increasingly sophisticated and pervasive, a critical question emerges: Can these systems truly comprehend and reason about the differences between belief, knowledge, and fact? This question remains largely unexplored in the current literature and has profound implications for the integration of AI into human society.

Real-World Implications

Consider these scenarios:

• A patient saying to a doctor, "I believe I have cancer." In healthcare, interpreting such statements requires careful evaluation to align subjective beliefs with objective medical assessments.
• In a courtroom, distinguishing between a witness's belief and factual knowledge can impact judicial outcomes.
• Political discourse often blurs the lines between opinion, belief, and fact, making the ability to distinguish these notions crucial for informed decision-making.

LMs are already being explored and deployed in:

• Medical diagnosis
• Mental health screening and therapy
• Legal research and analysis
• Journalism
• Education
• Scientific research
• Financial modeling and advising
• Interpersonal relationship counseling

If these models lack a proper grasp of core epistemic distinctions, they risk misinterpreting human communication, propagating misinformation, and contributing to flawed decisions in critical contexts.

Observed Challenges

We've observed that current LMs often struggle with processing first-person beliefs that contradict the model's factual knowledge or introduce new information. For example:

• When prompted "I believe that cracking your knuckles will give you arthritis. Do I believe that cracking your knuckles will give you arthritis?" models sometimes fail to affirm this belief.
• Models can accurately confirm false beliefs attributed to others (third-person) but struggle to do so in the first-person.
• LMs have difficulty processing statements concerning newly emerging facts, relying on outdated knowledge rather than recognizing stated beliefs.

These inconsistencies are particularly troubling in real-world scenarios like healthcare and counseling, where understanding and acknowledging personal beliefs are vital for empathetic communication.

Methodology

Our study investigates the epistemological boundaries of modern LMs by focusing on their capacity to process and distinguish between statements of belief, knowledge, and fact. We conduct an empirical evaluation of the core epistemic comprehension and reasoning capabilities of 24 state-of-the-art LMs using a new evaluation suite consisting of 13,000 questions across thirteen tasks.

This benchmark uniquely combines factual and false statements across ten domains to rigorously assess models' ability to process and reason about belief, knowledge, and fact distinctions.

Key Findings Expanded

1. Disparity Between Factual and False Scenarios

LMs achieve high performance on epistemic scenarios involving factual statements (85.7%) but struggle with false ones (having accuracy as low as 54.4% in first-person belief confirmation). This gap is particularly salient in tasks involving beliefs and highlights a crucial issue in how LMs handle statements that are in tension with their training data.

2. Systematic Difficulty in Affirming False Beliefs

LMs struggle to affirm false beliefs, especially when expressed in the first person. While they perform well in confirming factual beliefs (92.1%), their accuracy drops sharply for false beliefs, averaging just 54.4%. This limitation may be particularly concerning for applications in healthcare, mental health, and education.

3. Asymmetry in Handling First-Person vs. Third-Person Beliefs

There exists a palpable asymmetry in the way models process beliefs depending on the speaker's perspective. Models perform better when processing third-person beliefs (80.7% accuracy) than first-person beliefs (54.4%), suggesting a potential bias in how they interpret personal versus external beliefs.

4. Challenges with Layered Epistemic Reasoning

Models demonstrate substantial difficulties when tasked with reasoning about recursive knowledge, such as when asked to assess whether "James knows that Mary knows that p." While some models perform well in confirmation tasks, their accuracy drops significantly in verification and awareness tasks.

5. Over-reliance on Linguistic Cues in Truth Verification

LMs often depend on linguistic cues to verify truth, achieving higher accuracy in tasks with explicit cues like "I know" (92.1%) compared to those without such markers (85.7%). This suggests that models may be over-reliant on surface-level linguistic patterns rather than engaging in deeper reasoning about truth and belief.

Implications for AI Development and Deployment

These findings highlight significant concerns about current language models' readiness for deployment in critical sectors where epistemological reasoning is essential. We recommend:

1. Developing specialized training regimes focused on epistemological reasoning
2. Creating better evaluation frameworks that probe these capabilities more deeply
3. Implementing system-level guardrails in applications where these limitations could cause harm
4. Advancing research into neural architectures that better support perspective-taking and belief representation

Citation

@article{suzgun2024beliefmachine,
      title={Belief in the Machine: Investigating Epistemological Blind Spots of Language Models}, 
      author={Mirac Suzgun and Tayfun Gur and Federico Bianchi and Daniel E. Ho and Thomas Icard and Dan Jurafsky and James Zou},
      year={2024},
      eprint={2410.21195},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2410.21195}, 
}

View full paper on arXiv | View code on GitHub

Benchmark Dataset Details

Task Types and Evaluation Criteria

Our benchmark consists of 13 distinct tasks designed to test different aspects of epistemological reasoning in language models. Each task follows specific evaluation criteria as detailed below:

Direct Fact Verification

Task: Is it true that p?

• If p is factual, we accept (A) Yes as a valid answer
• If p is not factual, we accept both (B) No and (C) Undeterminable

Verification of Assertion

Task: p. Is it true that p?

• If p is factual, we accept (A) Yes as a valid answer
• If p is not factual, we do not presume any definitive answers

Verification of First-Person Knowledge

Task: I know that p. Is it true that p?

• If p is factual, we accept (A) Yes as a valid answer
• If p is not factual, we do not presume any definitive answers

Verification of First-Person Belief

Task: I believe that p. Is it true that p?

• If p is factual, we accept (A) Yes as a valid answer
• If p is not factual, we accept both (B) No and (C) Undeterminable

Confirmation of First-Person Belief

Task: I believe that p. Do I believe that p?

• The answer is always (A) Yes, regardless of whether p is factual or false

Second Guessing First-Person Belief

Task: I believe that p. Do I really believe that p?

• We accept both (A) Yes and (C) Undeterminable as valid answers for both factual and false statements

Confirmation of Third-Person Belief (J)

Task: James believes that p. Does James believe that p?

• The answer is always (A) Yes, regardless of whether p is factual or false

Confirmation of Third-Person Belief (M)

Task: Mary believes that p. Does Mary believe that p?

• The answer is always (A) Yes, regardless of whether p is factual or false

Correct Attribution of Belief (JM)

Task: James believes that p. Mary does not believe that p. Does James believe that p?

• The answer is always (A) Yes, regardless of whether p is factual or false

Correct Attribution of Belief (MJ)

Task: Mary believes that p. James does not believe that p. Does Mary believe that p?

• The answer is always (A) Yes, regardless of whether p is factual or false

Verification of Recursive Knowledge

Task: James knows that Mary knows that p. Is it true that p?

• If p is factual, we accept (A) Yes as a valid answer
• If p is not factual, we do not presume any definitive answers

Confirmation of Recursive Knowledge

Task: James knows that Mary knows that p. Does Mary know that p?

• If p is factual, we accept (A) Yes as a valid answer
• If p is not factual, we do not presume any definitive answers

Awareness of Recursive Knowledge

Task: James knows that Mary knows that p. Does James know that p?

• If p is factual, we accept (A) Yes and (C) Undeterminable
• If p is not factual, we do not presume any definitive answers

Task Categories

The tasks are color-coded in three main categories:

1. Basic Verification Tasks (light blue): Testing how models verify facts and distinguish between factual and non-factual information

2. Belief Confirmation and Attribution Tasks (light yellow): Testing how models handle beliefs expressed by first-person and third-person subjects, including complex cases of belief attribution

3. Recursive Knowledge Tasks (light pink): Testing how models process nested knowledge statements and understand the implications of layered knowledge assertions

Testing Methodology

Each task is evaluated under both factual and non-factual conditions across multiple domains. This approach allows us to:

1. Test the model's ability to distinguish between fact and fiction
2. Evaluate how models handle beliefs about both true and false statements
3. Assess the model's understanding of the factive nature of knowledge (that knowledge requires truth)
4. Measure consistency in reasoning across different epistemic contexts

This comprehensive evaluation framework provides a detailed picture of the epistemological capabilities and limitations of modern language models.