  Recent studies show that instruction tuning (IT) and reinforcement learning
from human feedback (RLHF) improve the abilities of large language models (LMs)
dramatically. While these tuning methods can help align models with human
objectives and generate high-quality text, not much is known about their
potential adverse effects. In this work, we investigate the effect of IT and
RLHF on decision making and reasoning in LMs, focusing on three cognitive
biases - the decoy effect, the certainty effect, and the belief bias - all of
which are known to influence human decision-making and reasoning. Our findings
highlight the presence of these biases in various models from the GPT-3,
Mistral, and T5 families. Notably, we find a stronger presence of biases in
models that have undergone instruction tuning, such as Flan-T5,
Mistral-Instruct, GPT3.5, and GPT4. Our work constitutes a step toward
comprehending cognitive biases in instruction-tuned LMs, which is crucial for
the development of more reliable and unbiased language models.
