EcoRank: Budget-Constrained Text Re-ranking Using Large Language Models

• URL: http://arxiv.org/abs/2402.10866v2
• Date: Tue, 28 May 2024 02:34:57 GMT
• Title: EcoRank: Budget-Constrained Text Re-ranking Using Large Language Models
• Authors: Muhammad Shihab Rashid, Jannat Ara Meem, Yue Dong, Vagelis Hristidis,
• Abstract summary: We study how to maximize the re-ranking performance given a budget. We propose a suite of budget-constrained methods to perform text re-ranking.
• Score: 6.109188517569139
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have achieved state-of-the-art performance in text re-ranking. This process includes queries and candidate passages in the prompts, utilizing pointwise, listwise, and pairwise prompting strategies. A limitation of these ranking strategies with LLMs is their cost: the process can become expensive due to API charges, which are based on the number of input and output tokens. We study how to maximize the re-ranking performance given a budget, by navigating the vast search spaces of prompt choices, LLM APIs, and budget splits. We propose a suite of budget-constrained methods to perform text re-ranking using a set of LLM APIs. Our most efficient method, called EcoRank, is a two-layered pipeline that jointly optimizes decisions regarding budget allocation across prompt strategies and LLM APIs. Our experimental results on four popular QA and passage reranking datasets show that EcoRank outperforms other budget-aware supervised and unsupervised baselines.

Related papers

• LaRA: Benchmarking Retrieval-Augmented Generation and Long-Context LLMs - No Silver Bullet for LC or RAG Routing [70.35888047551643]
  We present LaRA, a novel benchmark specifically designed to rigorously compare RAG and LC LLMs. LaRA encompasses 2,326 test cases across four practical QA task categories and three types of naturally occurring long texts. We find that the optimal choice between RAG and LC depends on a complex interplay of factors, including the model's parameter size, long-text capabilities, context length, task type, and the characteristics of the retrieved chunks.
  arXiv  Detail & Related papers  (2025-02-14T08:04:22Z)
• Universal Model Routing for Efficient LLM Inference [72.65083061619752]
  We consider the problem of dynamic routing, where new, previously unobserved LLMs are available at test time. We propose a new approach to this problem that relies on representing each LLM as a feature vector, derived based on predictions on a set of representative prompts. We prove that these strategies are estimates of a theoretically optimal routing rule, and provide an excess risk bound to quantify their errors.
  arXiv  Detail & Related papers  (2025-02-12T20:30:28Z)
• LLM Program Optimization via Retrieval Augmented Search [71.40092732256252]
  We propose a blackbox adaptation method called Retrieval Augmented Search (RAS) that performs beam search over candidate optimizations. We show that RAS performs 1.8$times$ better than prior state-of-the-art blackbox adaptation strategies. We also propose a method called AEGIS for improving interpretability by decomposing training examples into "atomic edits"
  arXiv  Detail & Related papers  (2025-01-31T06:34:47Z)
• Sliding Windows Are Not the End: Exploring Full Ranking with Long-Context Large Language Models [40.21540137079309]
  Long-context Language Models (LLMs) enable the full ranking of all passages within a single inference. We show that full ranking with long-context LLMs can deliver superior performance in the supervised fine-tuning setting. We propose a new complete listwise label construction approach and a novel importance-aware learning objective for full ranking.
  arXiv  Detail & Related papers  (2024-12-19T06:44:59Z)
• PickLLM: Context-Aware RL-Assisted Large Language Model Routing [0.5325390073522079]
  PickLLM is a lightweight framework that relies on Reinforcement Learning (RL) to route on-the-fly queries to available models. We demonstrate the speed of convergence for different learning rates and improvement in hard metrics such as cost per querying session and overall response latency.
  arXiv  Detail & Related papers  (2024-12-12T06:27:12Z)
• Self-Calibrated Listwise Reranking with Large Language Models [137.6557607279876]
  Large language models (LLMs) have been employed in reranking tasks through a sequence-to-sequence approach. This reranking paradigm requires a sliding window strategy to iteratively handle larger candidate sets. We propose a novel self-calibrated listwise reranking method, which aims to leverage LLMs to produce global relevance scores for ranking.
  arXiv  Detail & Related papers  (2024-11-07T10:31:31Z)
• MetaLLM: A High-performant and Cost-efficient Dynamic Framework for Wrapping LLMs [21.689490112983677]
  We introduce MetaLLM, a framework that dynamically routes each query to the optimal large language models (LLMs) for classification tasks. By framing the selection problem as a multi-armed bandit, MetaLLM balances prediction accuracy and cost efficiency under uncertainty. Our experiments, conducted on popular LLM platforms, showcase MetaLLM's efficacy in real-world scenarios.
  arXiv  Detail & Related papers  (2024-07-15T15:45:07Z)
• ReSLLM: Large Language Models are Strong Resource Selectors for Federated Search [35.44746116088232]
  Federated search will become increasingly pivotal in the context of Retrieval-Augmented Generation pipelines. Current SOTA resource selection methodologies rely on feature-based learning approaches. We propose ReSLLM to drive the selection of resources in federated search in a zero-shot setting.
  arXiv  Detail & Related papers  (2024-01-31T07:58:54Z)
• Query-Dependent Prompt Evaluation and Optimization with Offline Inverse RL [62.824464372594576]
  We aim to enhance arithmetic reasoning ability of Large Language Models (LLMs) through zero-shot prompt optimization. We identify a previously overlooked objective of query dependency in such optimization. We introduce Prompt-OIRL, which harnesses offline inverse reinforcement learning to draw insights from offline prompting demonstration data.
  arXiv  Detail & Related papers  (2023-09-13T01:12:52Z)
• OverPrompt: Enhancing ChatGPT through Efficient In-Context Learning [49.38867353135258]
  We propose OverPrompt, leveraging the in-context learning capability of LLMs to handle multiple task inputs. Our experiments show that OverPrompt can achieve cost-efficient zero-shot classification without causing significant detriment to task performance.
  arXiv  Detail & Related papers  (2023-05-24T10:08:04Z)
• Response Length Perception and Sequence Scheduling: An LLM-Empowered LLM Inference Pipeline [22.08897444328099]
  Large language models (LLMs) have revolutionized the field of AI, demonstrating unprecedented capacity across various tasks. In this paper, we propose an efficient LLM inference pipeline that harnesses the power of LLMs.
  arXiv  Detail & Related papers  (2023-05-22T15:36:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.