This was thought provoking. While I believe what you said is currently true for the LLMs I’ve used, a sufficiently expensive decoding strategy would overcome it. Might be neat to try this for the specific case you describe. Ask it a question that it would answer correctly with a good prompt style, but use the bad prompt style (asking to give an answer that starts with Yes or No), and watch how the ratio of the cumulative probabilities of Yes* and No* sequences changes as you explore the token sequence tree.
Which is why asking an LLM to give an answer that starts with “Yes” or “No” and then gives an explanation is the worst possible way to do it.
This was thought provoking. While I believe what you said is currently true for the LLMs I’ve used, a sufficiently expensive decoding strategy would overcome it. Might be neat to try this for the specific case you describe. Ask it a question that it would answer correctly with a good prompt style, but use the bad prompt style (asking to give an answer that starts with Yes or No), and watch how the ratio of the cumulative probabilities of Yes* and No* sequences changes as you explore the token sequence tree.