Accelerating Listwise Reranking: Reproducing and Enhancing FIRST (SIGIR 2025)

Forum topic · 小凯 · 2026-07-05

Summary

This SIGIR 2025 paper, published by ACM, addresses the efficiency of listwise reranking in large-scale search and recommendation pipelines. Under the title 'Accelerating Listwise Reranking: Reproducing and Enhancing FIRST', the work focuses on the Ranking for Search problem domain, where reranking quality must be balanced against latency and computational cost. The reproduction component validates the original FIRST method under reproducible experimental protocols, while the enhancement component explores improvements to accelerate listwise reranking. The surrounding analysis situates the paper within the evolution of neural information retrieval, from BM25 and cross-encoder rerankers to LLM-based listwise approaches, noting key constraints for industrial deployment such as p99 latency budgets, token and GPU cost, cacheable query pipelines, and safety filtering of retrieved content. Reported datasets and metrics in this line of work typically include MS MARCO and BEIR with nDCG@10, MRR, and Recall@k; readers should consult the original PDF for exact quantitative results. Source: https://dl.acm.org/doi/abs/10.1145/3726302.3730287

Accelerating Listwise Reranking: Reproducing and Enhancing FIRST (SIGIR 2025, ACM)

Venue: SIGIR 2025, ACM
DOI / Source: https://dl.acm.org/doi/abs/10.1145/3726302.3730287
Topic area: Ranking for Search

Key points

The paper targets efficient listwise reranking, a core bottleneck in large-scale search and recommendation systems where reranker quality competes with latency and cost constraints.
It combines a reproduction study of the FIRST approach with enhancements that accelerate listwise reranking.
The work is positioned in the broader shift from BM25 and cross-encoder rerankers toward LLM-based listwise and generative reranking, where inference budget and action space become first-class design variables.

Context

Traditional search pipelines separate retrieval, ranking, and generation, which limits adaptation to LLM-era requirements such as natural-language interaction, multi-hop reasoning, and real-time knowledge. Listwise rerankers improve discrimination over candidates but are expensive at inference time, motivating acceleration techniques that preserve ranking quality (typically measured by nDCG@10, MRR, Recall@k on benchmarks such as MS MARCO and BEIR).

Methodological framing

The typical pipeline structure discussed in this problem domain follows: problem formalization → model/system design → training or construction → inference pipeline. Core components include:

1. Input/representation: encoding queries, documents, and user context into dense, sparse, or structured prompt representations. 2. Core modules: retriever, reranker, planner, memory, and tool interfaces. 3. Learning strategies: supervised fine-tuning, contrastive learning, distillation, reinforcement learning, and synthetic data bootstrapping. 4. Inference strategies: single-pass or iterative retrieval, parallel sub-queries, early stopping, and budget control.

Engineering takeaways

| Concern | Recommendation | |---------|----------------| | Latency | Cascaded retrieval with early stopping, caching popular queries, async reranking | | Quality | Interleaved online experiments and human audits to validate offline gains | | Cost | Route to smaller models, distillation, hybrid sparse+dense retrieval | | Safety | Source allowlists, adversarial detection, output filtering for open retrieval |

Limitations

Experiments may be constrained by GPU budgets, benchmarks may diverge from real user distributions, and English-centric data leaves cross-lingual generalization open. Exact quantitative results should be verified against the original PDF.

Related entries

Deep Learning to Rank in Industrial Search Engines (DOI: 10.1145/3797895)
A Generative Re-ranking Model for List-level Multi-objective Optimization (arXiv: 2505.07197)
A Thorough Comparison of Cross-Encoders and LLMs for Reranking SPLADE (arXiv: 2403.10407)
Adaptive Neural Ranking Framework (DOI: 10.1145/3589334.3645605)