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Found 3 papers, 2 papers with code
Assessing top-$k$ preferences
To measure the performance of a ranker, we compare its output to this preferred ordering by applying a rank similarity measure. We demonstrate the practical feasibility of this approach by crowdsourcing partial preferences for the TREC 2019 Conversational Assistance Track, replacing NDCG with a new measure named "compatibility".
Shallow pooling for sparse labels
To test this observation, we employed crowdsourced workers to make preference judgments between the top item returned by a modern neural ranking stack and a judged relevant item.
A Search for Prompts: Generating Structured Answers from Contracts
In many legal processes being able to action on the concrete implication of a legal question can be valuable to automating human review or signalling certain conditions (e. g., alerts around automatic renewal).
