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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Large language models and their big bullshit potential.

Sarah A Fisher¹

¹School of English, Communication and Philosophy, Cardiff University, Cardiff, UK.

Ethics and Information Technology

|October 7, 2024

Summary

This summary is machine-generated.

Large language models (LLMs) can "bullshit" by generating plausible content without truth assessment. However, with proper safeguards, LLMs need not produce bullshit, depending on their design.

Keywords:

Bullshit ChatGPT Large language models Truth

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Area of Science:

Artificial Intelligence
Philosophy of Language
Computational Linguistics

Background:

Large language models (LLMs) are increasingly prevalent across various applications.
Concerns exist that LLMs may generate convincing but untruthful content, a behavior termed 'bullshitting'.
Bullshitting undermines truthfulness and the perceived value of truth in discourse.

Purpose of the Study:

To investigate whether large language models engage in bullshitting.
To determine if LLMs' propensity to bullshit is inherent or controllable.

Main Methods:

Analysis of LLM output in response to fact-seeking prompts.
Philosophical examination of the definition and implications of bullshitting.
Consideration of potential technical and design-based interventions.

Main Results:

LLMs can issue propositional content without prior truth assessment, aligning with a definition of bullshitting.
This behavior is not an inherent characteristic of all LLMs.
The propensity for LLMs to bullshit is contingent on their specific architecture and implemented guardrails.

Conclusions:

Large language models possess the capacity to bullshit.
Implementing appropriate guardrails can mitigate the risk of LLMs generating bullshit.
The behavior of LLMs, similar to human speakers, is influenced by their internal configuration and external controls.