Papers › Instruction Distillation Makes Large Language Models Efficient Zero-shot Rankers

Instruction Distillation Makes Large Language Models Efficient Zero-shot Rankers

2 Nov 2023arXiv:2311.01555archive 2025-07-28

Weiwei Sun, Zheng Chen, Xinyu Ma, Lingyong Yan, Shuaiqiang Wang, Pengjie Ren, Zhumin Chen, Dawei Yin, Zhaochun Ren

Recent studies have demonstrated the great potential of Large Language Models (LLMs) serving as zero-shot relevance rankers. The typical approach involves making comparisons between pairs or lists of documents. Although effective, these listwise and pairwise methods are not efficient and also heavily rely on intricate prompt engineering. To tackle this problem, we introduce a novel instruction distillation method. The key idea is to distill the pairwise ranking ability of open-sourced LLMs to a simpler but more efficient pointwise ranking. Specifically, given the same LLM, we first rank documents using the effective pairwise approach with complex instructions, and then distill the teacher predictions to the pointwise approach with simpler instructions. Evaluation results on the BEIR, TREC, and ReDial datasets demonstrate that instruction distillation can improve efficiency by 10 to 100x and also enhance the ranking performance of LLMs. Furthermore, our approach surpasses the performance of existing supervised methods like monoT5 and is on par with the state-of-the-art zero-shot methods. The code to reproduce our results is available at www.github.com/sunnweiwei/RankGPT.

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convert_messages_to_prompt sunnweiwei/rankgpt/rank_gpt.py official repository ran Apache-2.0 (permissive) · 8e9717b5010ed9a8 · report
gather_tensors sunnweiwei/rankgpt/InstructDistill/instruction_distill.py official repository ran fingerprinted Apache-2.0 (permissive) · 1eb76fdb17d065e3 · report
get_prefix_prompt sunnweiwei/rankgpt/rank_gpt.py official repository ran Apache-2.0 (permissive) · 64789829d440d8a9 · report
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lambdaRank_scheme sunnweiwei/rankgpt/rank_loss.py official repository ran fingerprinted Apache-2.0 (permissive) · 9c71991dafef6c40 · report
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receive_response sunnweiwei/rankgpt/specialization.py official repository ran Apache-2.0 (permissive) · 103f50dcc89657c1 · report
remove_duplicate sunnweiwei/rankgpt/trec_eval.py official repository ran fingerprinted Apache-2.0 (permissive) · 4fc539f7640d8633 · report
run_retriever sunnweiwei/rankgpt/rank_gpt.py official repository ran Apache-2.0 (permissive) · 638bf99dbd386233 · report
run_retriever sunnweiwei/rankgpt/InstructDistill/bm25_retrieval.py official repository ran Apache-2.0 (permissive) · 43e84c75d62b7d6c · report
split_data sunnweiwei/rankgpt/InstructDistill/instruction_distill.py official repository ran fingerprinted Apache-2.0 (permissive) · 654ad1edd7372dd6 · report
get_qrels_file sunnweiwei/rankgpt/trec_eval.py official repository unverified Apache-2.0 (permissive) · ce1221b963c84b7c · report
ndcgLoss2_scheme sunnweiwei/rankgpt/rank_loss.py official repository unverified Apache-2.0 (permissive) · c1fb53abb507ff8e · report
query_generation sunnweiwei/rankgpt/pointwise.py official repository unverified Apache-2.0 (permissive) · a6d651ee8a87c18b · report
relevance_generation sunnweiwei/rankgpt/pointwise.py official repository unverified Apache-2.0 (permissive) · 7586cb0204e57b53 · report

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