Machine Bullshit

Bullshit Index (BI): The authors define BI as the inverse correlation between a model’s internal belief and its explicit claims. A higher BI indicates greater indifference to truth. RLHF significantly increases BI, suggesting that LLMs fine-tuned for user satisfaction tend to disregard their own epistemic signals.

RLHF encourages misleading behavior: On the Marketplace benchmark, RLHF causes LLMs to make more positively deceptive statements in scenarios with negative or unknown ground truth, e.g., stating a product has a desirable feature even when uncertain or incorrect. Paltering and unverified claims increased by 57.8% and 55.6% respectively.

Prompting strategies amplify bullshit: CoT prompting consistently increases empty rhetoric (+20.9%) and paltering (+11.5%) in GPT-4o-mini. Similarly, Principal-Agent framing, introducing conflicting incentives, elevates all bullshit categories across models, particularly unverified claims.

Political contexts are prone to weasel words: In politically sensitive prompts, models like GPT-4o-mini and Qwen-2.5-72b rely heavily on vague qualifiers (up to 91% of responses in conspiracy-related questions). Explicitly adding political viewpoints further boosts subtle deception like paltering and empty rhetoric.

Paltering is most harmful post-RLHF: Regression analysis shows paltering becomes the most damaging behavior after RLHF, significantly degrading user decision quality, even more than unverified claims.