Cost-Effective Online Multi-LLM Selection with Versatile Reward Models

• URL: http://arxiv.org/abs/2405.16587v2
• Date: Wed, 02 Oct 2024 13:22:27 GMT
• Title: Cost-Effective Online Multi-LLM Selection with Versatile Reward Models
• Authors: Xiangxiang Dai, Jin Li, Xutong Liu, Anqi Yu, John C. S. Lui,
• Abstract summary: We introduce the textitC2MAB-V, an online model for selecting and using large language models (LLMs) textitC2MAB-V is specifically tailored for various collaborative task types with different reward models. We show that textitC2MAB-V effectively balances performance and cost-efficiency with nine LLMs for three application scenarios.
• Score: 30.892090566736652
• License:
• Abstract: With the rapid advancement of large language models (LLMs), the diversity of multi-LLM tasks and the variability in their pricing structures have become increasingly important, as costs can vary greatly between different LLMs. To tackle these challenges, we introduce the \textit{C2MAB-V}, a \underline{C}ost-effective \underline{C}ombinatorial \underline{M}ulti-armed \underline{B}andit with \underline{V}ersatile reward models for optimal LLM selection and usage. This online model differs from traditional static approaches or those reliant on a single LLM without cost consideration. With multiple LLMs deployed on a scheduling cloud and a local server dedicated to handling user queries, \textit{C2MAB-V} facilitates the selection of multiple LLMs over a combinatorial search space, specifically tailored for various collaborative task types with different reward models. Based on our designed online feedback mechanism and confidence bound technique, \textit{C2MAB-V} can effectively address the multi-LLM selection challenge by managing the exploration-exploitation trade-off across different models, while also balancing cost and reward for diverse tasks. The NP-hard integer linear programming problem for selecting multiple LLMs with trade-off dilemmas is addressed by: i) decomposing the integer problem into a relaxed form by the local server, ii) utilizing a discretization rounding scheme that provides optimal LLM combinations by the scheduling cloud, and iii) continual online updates based on feedback. Theoretically, we prove that \textit{C2MAB-V} offers strict guarantees over versatile reward models, matching state-of-the-art results for regret and violations in some degenerate cases. Empirically, we show that \textit{C2MAB-V} effectively balances performance and cost-efficiency with nine LLMs for three application scenarios.

Related papers

• Scaling Autonomous Agents via Automatic Reward Modeling And Planning [52.39395405893965]
  Large language models (LLMs) have demonstrated remarkable capabilities across a range of tasks. However, they still struggle with problems requiring multi-step decision-making and environmental feedback. We propose a framework that can automatically learn a reward model from the environment without human annotations.
  arXiv  Detail & Related papers  (2025-02-17T18:49:25Z)
• LLM-Lasso: A Robust Framework for Domain-Informed Feature Selection and Regularization [59.75242204923353]
  We introduce LLM-Lasso, a framework that leverages large language models (LLMs) to guide feature selection in Lasso regression. LLMs generate penalty factors for each feature, which are converted into weights for the Lasso penalty using a simple, tunable model. Features identified as more relevant by the LLM receive lower penalties, increasing their likelihood of being retained in the final model.
  arXiv  Detail & Related papers  (2025-02-15T02:55:22Z)
• LLM Bandit: Cost-Efficient LLM Generation via Preference-Conditioned Dynamic Routing [3.090041654375235]
  We present a novel framework that formulates the LLM selection process as a multi-armed bandit problem. Our approach incorporates a preference-conditioned dynamic routing mechanism, allowing users to specify their preferences at inference time. Our method achieves significant improvements in both accuracy and cost-effectiveness across various LLM platforms.
  arXiv  Detail & Related papers  (2025-02-04T22:09:43Z)
• 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)
• LLM Chain Ensembles for Scalable and Accurate Data Annotation [1.7388851660609117]
  Large language models (LLMs) can perform zero-shot classification, but large-scale deployment can be expensive. This paper introduces an LLM chain ensemble methodology that aligns multiple LLMs in a sequence, routing data subsets to subsequent models. Our results show that the chain ensemble method often exceeds the performance of the best individual model in the chain and achieves substantial cost savings.
  arXiv  Detail & Related papers  (2024-10-16T20:03:51Z)
• SoupLM: Model Integration in Large Language and Multi-Modal Models [51.12227693121004]
  Training large language models (LLMs) requires significant computing resources. Existing publicly available LLMs are typically pre-trained on diverse, privately curated datasets spanning various tasks.
  arXiv  Detail & Related papers  (2024-07-11T05:38:15Z)
• Delta-CoMe: Training-Free Delta-Compression with Mixed-Precision for Large Language Models [79.46938238953916]
  Fine-tuning large language models (LLMs) to diverse applications is crucial to meet complex demands. Recent studies suggest decomposing a fine-tuned LLM into a base model and corresponding delta weights, which are then compressed using low-rank or low-bit approaches to reduce costs. In this work, we observe that existing low-rank and low-bit compression methods can significantly harm the model performance for task-specific fine-tuned LLMs.
  arXiv  Detail & Related papers  (2024-06-13T07:57:27Z)
• Any-Precision LLM: Low-Cost Deployment of Multiple, Different-Sized LLMs [3.450141240227484]
  We propose a lightweight method for any-precision quantization of Large Language Models (LLMs) Our solution significantly reduces the high costs of deploying multiple, different-sized LLMs. All the supported LLMs with varying bit-widths demonstrate state-of-the-art model quality and inference throughput.
  arXiv  Detail & Related papers  (2024-02-16T09:06:06Z)
• LLM-Pruner: On the Structural Pruning of Large Language Models [65.02607075556742]
  Large language models (LLMs) have shown remarkable capabilities in language understanding and generation. We tackle the compression of LLMs within the bound of two constraints: being task-agnostic and minimizing the reliance on the original training dataset. Our method, named LLM-Pruner, adopts structural pruning that selectively removes non-critical coupled structures.
  arXiv  Detail & Related papers  (2023-05-19T12:10:53Z)
• Controllable Pareto Multi-Task Learning [55.945680594691076]
  A multi-task learning system aims at solving multiple related tasks at the same time. With a fixed model capacity, the tasks would be conflicted with each other, and the system usually has to make a trade-off among learning all of them together. This work proposes a novel controllable multi-task learning framework, to enable the system to make real-time trade-off control among different tasks with a single model.
  arXiv  Detail & Related papers  (2020-10-13T11:53:55Z)

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.