MetaLLM: A High-performant and Cost-efficient Dynamic Framework for Wrapping LLMs

• URL: http://arxiv.org/abs/2407.10834v2
• Date: Wed, 24 Jul 2024 08:14:50 GMT
• Title: MetaLLM: A High-performant and Cost-efficient Dynamic Framework for Wrapping LLMs
• Authors: Quang H. Nguyen, Duy C. Hoang, Juliette Decugis, Saurav Manchanda, Nitesh V. Chawla, Khoa D. Doan,
• Abstract summary: 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.
• Score: 21.689490112983677
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid progress in machine learning (ML) has brought forth many large language models (LLMs) that excel in various tasks and areas. These LLMs come with different abilities and costs in terms of computation or pricing. Since the demand for each query can vary, e.g., because of the queried domain or its complexity, defaulting to one LLM in an application is not usually the best choice, whether it is the biggest, priciest, or even the one with the best average test performance. Consequently, picking the right LLM that is both accurate and cost-effective for an application remains a challenge. In this paper, we introduce MetaLLM, a framework that dynamically and intelligently routes each query to the optimal LLM (among several available LLMs) for classification tasks, achieving significantly improved accuracy and cost-effectiveness. 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 such as OpenAI's GPT models, Amazon's Titan, Anthropic's Claude, and Meta's LLaMa, showcase MetaLLM's efficacy in real-world scenarios, laying the groundwork for future extensions beyond classification tasks.

Related papers

• WALL-E: World Alignment by Rule Learning Improves World Model-based LLM Agents [55.64361927346957]
  We propose a neurosymbolic approach to learn rules gradient-free through large language models (LLMs) Our embodied LLM agent "WALL-E" is built upon model-predictive control (MPC) On open-world challenges in Minecraft and ALFWorld, WALL-E achieves higher success rates than existing methods.
  arXiv  Detail & Related papers  (2024-10-09T23:37:36Z)
• SelectLLM: Query-Aware Efficient Selection Algorithm for Large Language Models [8.558834738072363]
  Large language models (LLMs) have gained increased popularity due to their remarkable success across various tasks. However, individual LLMs have limitations when applied to complex tasks because of such factors as training biases, model sizes, and the datasets used. We introduce SelectLLM, a novel algorithm that directs input queries to the most suitable subset of LLMs from a large pool.
  arXiv  Detail & Related papers  (2024-08-16T06:11:21Z)
• Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning [53.6472920229013]
  Large Language Models (LLMs) have demonstrated impressive capability in many natural language tasks. LLMs are prone to produce errors, hallucinations and inconsistent statements when performing multi-step reasoning. We introduce Q*, a framework for guiding LLMs decoding process with deliberative planning.
  arXiv  Detail & Related papers  (2024-06-20T13:08:09Z)
• Meta Reasoning for Large Language Models [58.87183757029041]
  We introduce Meta-Reasoning Prompting (MRP), a novel and efficient system prompting method for large language models (LLMs) MRP guides LLMs to dynamically select and apply different reasoning methods based on the specific requirements of each task. We evaluate the effectiveness of MRP through comprehensive benchmarks.
  arXiv  Detail & Related papers  (2024-06-17T16:14:11Z)
• OptLLM: Optimal Assignment of Queries to Large Language Models [12.07164196530872]
  We propose a framework for addressing the cost-effective query allocation problem for large language models (LLMs) Our framework, named OptLLM, provides users with a range of optimal solutions to choose from, aligning with their budget constraints and performance preferences. To evaluate the effectiveness of OptLLM, we conduct extensive experiments on various types of tasks, including text classification, question answering, sentiment analysis, reasoning, and log parsing.
  arXiv  Detail & Related papers  (2024-05-24T01:05:37Z)
• Towards Efficient LLM Grounding for Embodied Multi-Agent Collaboration [70.09561665520043]
  We propose a novel framework for multi-agent collaboration that introduces Reinforced Advantage feedback (ReAd) for efficient self-refinement of plans. We provide theoretical analysis by extending advantage-weighted regression in reinforcement learning to multi-agent systems. Experiments on Over-AI and a difficult variant of RoCoBench show that ReAd surpasses baselines in success rate, and also significantly decreases the interaction steps of agents.
  arXiv  Detail & Related papers  (2024-05-23T08:33:19Z)
• Efficient Multimodal Large Language Models: A Survey [60.7614299984182]
  Multimodal Large Language Models (MLLMs) have demonstrated remarkable performance in tasks such as visual question answering, visual understanding and reasoning. The extensive model size and high training and inference costs have hindered the widespread application of MLLMs in academia and industry. This survey provides a comprehensive and systematic review of the current state of efficient MLLMs.
  arXiv  Detail & Related papers  (2024-05-17T12:37:10Z)
• SMART: Automatically Scaling Down Language Models with Accuracy Guarantees for Reduced Processing Fees [21.801053526411415]
  Large Language Models (LLMs) have significantly boosted performance in natural language processing (NLP) tasks. The deployment of high-performance LLMs incurs substantial costs, primarily due to the increased number of parameters aimed at enhancing model performance. We introduce SMART, a novel framework designed to minimize the inference costs of NLP tasks while ensuring sufficient result quality.
  arXiv  Detail & Related papers  (2024-03-11T17:45:47Z)
• Cache me if you Can: an Online Cost-aware Teacher-Student framework to Reduce the Calls to Large Language Models [13.799197575126442]
  Small and medium-sized enterprises (SMEs) cannot afford the cost of creating large task-specific training datasets. Third-party services that allow them to prompt Large Language Models currently require a payment per call. We propose a framework that allows reducing the calls to LLMs by caching previous responses and using them to train a local inexpensive model.
  arXiv  Detail & Related papers  (2023-10-20T10:05:07Z)
• Generative Multimodal Entity Linking [24.322540112710918]
  Multimodal Entity Linking (MEL) is the task of mapping mentions with multimodal contexts to referent entities from a knowledge base. Existing MEL methods mainly focus on designing complex multimodal interaction mechanisms and require fine-tuning all model parameters. We propose GEMEL, a Generative Multimodal Entity Linking framework based on Large Language Models (LLMs) Our framework is compatible with any off-the-shelf language model, paving the way towards an efficient and general solution.
  arXiv  Detail & Related papers  (2023-06-22T07:57:19Z)
• 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)

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.