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Two-Turn Debate Does Not Help Humans Answer Hard Reading Comprehension Questions
Alicia Parrish · Harsh Trivedi · Nikita Nangia · Jason Phang · Vishakh Padmakumar · Amanpreet Singh Saimbhi · Samuel Bowman

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in Workshop on Machine Learning Safety »

The use of language-model-based question-answering systems to aid humans in completing difficult tasks is limited, in part, by the unreliability of the text these systems generate. Using hard multiple-choice reading comprehension questions as a testbed, we assess whether presenting humans with arguments for two competing answer options, where one is correct and the other is incorrect, allows human judges to perform more accurately, even when one of the arguments is unreliable and deceptive. If this is helpful, we may be able to increase our justified trust in language-model-based systems by asking them to produce these arguments where needed. Previous research has shown that just a single turn of arguments in this format is not helpful to humans. However, as debate settings are characterized by a back-and-forth dialogue, we follow up on previous results to test whether adding a second round of counter-arguments is helpful to humans. We find that, regardless of whether they have access to arguments or not, humans perform similarly on our task. These findings suggest that, in the case of answering reading comprehension questions, debate is not a helpful format.

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Author Information

Alicia Parrish (Google)
Harsh Trivedi (Stony Brook University)
Nikita Nangia (NYU)
Jason Phang (New York University)
Vishakh Padmakumar (New York University)
Amanpreet Singh Saimbhi (New York University)
Samuel Bowman (NYU + Anthropic)

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