Accelerating Listwise Reranking: Reproducing and Enhancing FIRST
Bibliographic record
Abstract
Large language models (LLMs) have emerged as powerful listwise rerankers but remain prohibitively slow for many real-world applications. What's more, training on the language modeling (LM) objective is not intrinsically aligned with reranking tasks. To address these challenges, FIRST, a novel approach for listwise reranking, integrates a learning-to-rank objective and leverages only the logits of the first generated token for reranking, significantly reducing computational overhead while preserving effectiveness. We systematically evaluate the capabilities and limitations of FIRST. By extending its evaluation to TREC Deep Learning collections (DL19-23), we show that FIRST achieves robust out-of-domain effectiveness. Through training FIRST on a variety of backbone models, we demonstrate its generalizability across different model architectures, and achieve effectiveness surpassing the original implementation. Further analysis of the interaction between FIRST and various first-stage retrievers reveals diminishing returns akin to traditional LLM rerankers. A comprehensive latency study confirms that FIRST consistently delivers a 40% efficiency gain over traditional rerankers without sacrificing effectiveness. Notably, while LM training implicitly improves zero-shot single-token reranking, our experiments also highlight potential conflicts between LM pre-training and subsequent fine-tuning on the FIRST objective. These findings pave the way for more efficient and effective listwise reranking in future applications. Our code is available at: https://rankllm.ai.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".