Wednesday, June 26, 2024

How to bullshit about bullshit

Take a look at a paper ChatGPT is bullshit, by Michael Townsen Hicks, James Humphries, Joe Slater. Other than a catchy title, the paper has a few problems of its own.

1. Focus on terminology over utility:

The paper spends considerable effort arguing that the outputs of large language models (LLMs) like ChatGPT should be classified as "bullshit" rather than "hallucinations." While this may be an interesting philosophical distinction, it arguably does little to advance our understanding of the practical implications and applications of these technologies. The authors could have devoted more attention to analyzing the actual utility and potential use cases of LLMs, which would likely be more valuable to policymakers and the public.

2. Limited practical insights:

By focusing primarily on categorizing the nature of LLM outputs, the paper misses an opportunity to provide meaningful insights into how these technologies can be effectively and responsibly utilized. A more constructive approach might involve examining specific contexts where LLMs prove useful and where they fall short, rather than broadly labeling their output as "bullshit."

3. Potential for misdirection:

While the authors argue that the term "hallucinations" is misleading, replacing it with "bullshit" may not necessarily lead to a clearer understanding of LLM behavior. Both terms are metaphorical and could potentially misguide readers about the true nature and capabilities of these systems. A more technical and nuanced explanation of how LLMs function and their limitations might be more informative.

4. Overlooking nuance:

The paper seems to take a binary approach – either LLMs are concerned with truth (which they argue against) or they are producing "bullshit." This oversimplification may overlook the nuanced ways in which LLMs can be useful for various tasks, even if they don't have an inherent concern for truth in the way humans do.

5. Lack of actionable recommendations:

While the authors critique the use of the term "hallucinations," they don't offer clear, actionable recommendations for how to better communicate about LLMs to policymakers and the public. A more constructive approach would be to propose specific ways to educate stakeholders about the capabilities and limitations of these technologies.

6. Missing the broader context:

By focusing narrowly on the philosophical categorization of LLM outputs, the paper misses an opportunity to discuss the broader implications of these technologies on society, economy, and various industries. A more comprehensive analysis of the impact and potential of LLMs would likely be more valuable to readers.

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