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Rank-DistiLLM: Closing the Effectiveness Gap Between Cross-Encoders and LLMs for Passage Re-Ranking by Schlatt, Ferdinand; Fröbe, Maik; Scells, Harrisen; Zhuang, Shengyao; Koopman, Bevan; Zuccon, Guido; Stein, Benno; Potthast, Martin; Hagen, Matthias is a scholarly article available to read on EtoBox.

What is Rank-DistiLLM: Closing the Effectiveness Gap Between Cross-Encoders and LLMs for Passage Re-Ranking about?

Cross-encoders distilled from large language models (LLMs) are often more effective re-rankers than cross-encoders fine-tuned on manually labeled data. However, distilled models do not match the effectiveness of their teacher LLMs. We hypothesize that this effectiveness gap is due to the fact that previous work has not applied the best-suited methods for fine-tuning cross-encoders on manually labeled data (e.g., hard-negative sampling, deep sampling, and listwise loss functions). To close this gap, we create a new dataset, Rank-DistiLLM. Cross-encoders trained on Rank-DistiLLM achieve the effectiveness of LLMs while being up to 173 times faster and 24 times more memory efficient. Our code and data is available at https://github.com/webis-de/ECIR-25.

Author

Schlatt, Ferdinand; Fröbe, Maik; Scells, Harrisen; Zhuang, Shengyao; Koopman, Bevan; Zuccon, Guido; Stein, Benno; Potthast, Martin; Hagen, Matthias

Published

2024

Language

EN