Machine Bullshit: Characterizing the Emergent Disregard for Truth in
  Large Language Models
Bullshit, as conceptualized by philosopher Harry Frankfurt, refers to
statements made without regard to their truth value. While previous work has
explored large language model (LLM) hallucination and sycophancy, we propose
machine bullshit as an overarching conceptual framework that can allow
researchers to characterize the broader phenomenon of emergent loss of
truthfulness in LLMs and shed light on its underlying mechanisms. We introduce
the Bullshit Index, a novel metric quantifying LLMs' indifference to truth, and
propose a complementary taxonomy analyzing four qualitative forms of bullshit:
empty rhetoric, paltering, weasel words, and unverified claims. We conduct
empirical evaluations on the Marketplace dataset, the Political Neutrality
dataset, and our new BullshitEval benchmark (2,400 scenarios spanning 100 AI
assistants) explicitly designed to evaluate machine bullshit. Our results
demonstrate that model fine-tuning with reinforcement learning from human
feedback (RLHF) significantly exacerbates bullshit and inference-time
chain-of-thought (CoT) prompting notably amplify specific bullshit forms,
particularly empty rhetoric and paltering. We also observe prevalent machine
bullshit in political contexts, with weasel words as the dominant strategy. Our
findings highlight systematic challenges in AI alignment and provide new
insights toward more truthful LLM behavior.
68708d7ac8391850d609789f
Kaiqu Liang
claimed_verified
2025-07-21T08:30:34.445Z
650237b5d1b0b0db4f29ae8a
/avatars/8f92cf8f3f1ddb45c2c58c4a59ce4633.svg
KAIQU LIANG
user
kaiquliang
68708d7ac8391850d60978a0
Haimin Hu
68708d7ac8391850d60978a1
Xuandong Zhao
68708d7ac8391850d60978a2
Dawn Song
68708d7ac8391850d60978a3
Thomas L. Griffiths
68708d7ac8391850d60978a4
Jaime Fernández Fisac
Machine Bullshit: Characterizing the Emergent Disregard for Truth in
  Large Language Models
Bullshit, as conceptualized by philosopher Harry Frankfurt, refers to
statements made without regard to their truth value. While previous work has
explored large language model (LLM) hallucination and sycophancy, we propose
machine bullshit as an overarching conceptual framework that can allow
researchers to characterize the broader phenomenon of emergent loss of
truthfulness in LLMs and shed light on its underlying mechanisms. We introduce
the Bullshit Index, a novel metric quantifying LLMs' indifference to truth, and
propose a complementary taxonomy analyzing four qualitative forms of bullshit:
empty rhetoric, paltering, weasel words, and unverified claims. We conduct
empirical evaluations on the Marketplace dataset, the Political Neutrality
dataset, and our new BullshitEval benchmark (2,400 scenarios spanning 100 AI
assistants) explicitly designed to evaluate machine bullshit. Our results
demonstrate that model fine-tuning with reinforcement learning from human
feedback (RLHF) significantly exacerbates bullshit and inference-time
chain-of-thought (CoT) prompting notably amplify specific bullshit forms,
particularly empty rhetoric and paltering. We also observe prevalent machine
bullshit in political contexts, with weasel words as the dominant strategy. Our
findings highlight systematic challenges in AI alignment and provide new
insights toward more truthful LLM behavior.
Research introduces machine bullshit as a framework to assess truthfulness in LLMs, using the Bullshit Index and taxonomy, and finds RLHF and CoT prompting increase specific bullshit forms.
large language model LLM machine bullshit Bullshit Index empty rhetoric paltering weasel words unverified claims reinforcement learning from human feedback RLHF chain-of-thought CoT AI alignment