  Aligning large language models (LLMs) with human values and intents
critically involves the use of human or AI feedback. While dense feedback
annotations are expensive to acquire and integrate, sparse feedback presents a
structural design choice between ratings (e.g., score Response A on a scale of
1-7) and rankings (e.g., is Response A better than Response B?). In this work,
we analyze the effect of this design choice for the alignment and evaluation of
LLMs. We uncover an inconsistency problem wherein the preferences inferred from
ratings and rankings significantly disagree 60% for both human and AI
annotators. Our subsequent analysis identifies various facets of annotator
biases that explain this phenomena, such as human annotators would rate denser
responses higher while preferring accuracy during pairwise judgments. To our
surprise, we also observe that the choice of feedback protocol also has a
significant effect on the evaluation of aligned LLMs. In particular, we find
that LLMs that leverage rankings data for alignment (say model X) are preferred
over those that leverage ratings data (say model Y), with a rank-based
evaluation protocol (is X/Y's response better than reference response?) but not
with a rating-based evaluation protocol (score Rank X/Y's response on a scale
of 1-7). Our findings thus shed light on critical gaps in methods for
evaluating the real-world utility of language models and their strong
dependence on the feedback protocol used for alignment. Our code and data are
available at https://github.com/Hritikbansal/sparse_feedback.
