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hLLM: LLM reranking accelerated by 64x using Hungarian algorithm

By PulseAugur Editorial · [1 sources] ·

Researchers have developed hLLM, a novel decoding strategy for large language models (LLMs) that significantly speeds up generative reranking. By treating the output as a permutation problem solvable with the Hungarian algorithm, hLLM achieves a $64\times$ speed-up in end-to-end inference, reaching 28 ms. This method maintains ranking quality comparable to teacher models and connects generative ranking to combinatorial optimization. AI

IMPACT This method could enable real-time applications for LLM-based ranking systems by drastically reducing inference time.

RANK_REASON The cluster contains a research paper detailing a new method for LLM decoding. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

hLLM: LLM reranking accelerated by 64x using Hungarian algorithm

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The cluster contains a research paper detailing a new method for LLM decoding. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Emil Laftchiev, Prachi Agrawal, Moe Kayali, Bixing Yan, Qi Xu, Zijie Lei, Chen Qiu, Zhi Hua, Ke Li, Luke Simon ·

    hLLM: Single Pass Decoding for Generative Reranking

    arXiv:2609.01807v1 Announce Type: cross Abstract: Large language models (LLMs) achieve state-of-the-art generative ranking quality, but the ranking they produce must be decoded, and autoregressive decoding spends one sequential forward pass per emitted token. We observe that the …

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