Top-Down Partitioning for Efficient List-Wise Ranking
CoRR(2024)
Abstract
Large Language Models (LLMs) have significantly impacted many facets of
natural language processing and information retrieval. Unlike previous
encoder-based approaches, the enlarged context window of these generative
models allows for ranking multiple documents at once, commonly called list-wise
ranking. However, there are still limits to the number of documents that can be
ranked in a single inference of the model, leading to the broad adoption of a
sliding window approach to identify the k most relevant items in a ranked list.
We argue that the sliding window approach is not well-suited for list-wise
re-ranking because it (1) cannot be parallelized in its current form, (2) leads
to redundant computational steps repeatedly re-scoring the best set of
documents as it works its way up the initial ranking, and (3) prioritizes the
lowest-ranked documents for scoring rather than the highest-ranked documents by
taking a bottom-up approach. Motivated by these shortcomings and an initial
study that shows list-wise rankers are biased towards relevant documents at the
start of their context window, we propose a novel algorithm that partitions a
ranking to depth k and processes documents top-down. Unlike sliding window
approaches, our algorithm is inherently parallelizable due to the use of a
pivot element, which can be compared to documents down to an arbitrary depth
concurrently. In doing so, we reduce the number of expected inference calls by
around 33
approaches across multiple strong re-rankers.
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