SelectLLM: Query-Aware Efficient Selection Algorithm for Large Language Models

• URL: http://arxiv.org/abs/2408.08545v2
• Date: Mon, 30 Dec 2024 05:01:44 GMT
• Title: SelectLLM: Query-Aware Efficient Selection Algorithm for Large Language Models
• Authors: Kaushal Kumar Maurya, KV Aditya Srivatsa, Ekaterina Kochmar,
• Abstract summary: Large language models (LLMs) have seen widespread adoption due to their remarkable performance across various applications. These individual LLMs show limitations in generalization and performance on complex tasks due to inherent training biases, model size constraints, and the quality or diversity of pre-training datasets. We introduce SelectLLM, which efficiently directs input queries to the most suitable subset of LLMs from a large pool.
• Score: 8.558834738072363
• License:
• Abstract: Large language models (LLMs) have seen widespread adoption due to their remarkable performance across various applications, driving the accelerated development of a large number of diverse LLMs. However, these individual LLMs show limitations in generalization and performance on complex tasks due to inherent training biases, model size constraints, and the quality or diversity of pre-training datasets. A promising direction is to efficiently harness the diverse capabilities of LLMs to overcome these individual limitations. To address these limitations, we introduce a novel LLM selection algorithm called SelectLLM, which efficiently directs input queries to the most suitable subset of LLMs from a large pool, ensuring that the selected models collectively provide accurate responses. SelectLLM employs a multi-label classifier and policy based on the classifier's predictions and confidence scores in selecting an optimal, query-aware, and lightweight subset of LLMs. Our findings indicate that the proposed model outperforms existing ensemble-based baselines and achieves competitive performance with similarly sized top-performing LLMs while maintaining efficiency. Specifically, it achieves a huge reduction in inference latency on two challenging reasoning benchmarks: 13% on GSM8K and 70% on MMLU, compared to the top-performing baselines. Also, we establish a theoretical upper bound by an oracle with LLMs and explore in-depth linguistic analysis to understand the performance gap between Oracle and SelectLLM.

Related papers

• 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)
• OptiSeq: Ordering Examples On-The-Fly for In-Context Learning [8.603219414567084]
  We introduce OptiSeq, a purely inference-time, dataset-free optimization method that efficiently determines the best example order. We show that OptiSeq improves accuracy by 5.5 - 10.5 percentage points across multiple tasks.
  arXiv  Detail & Related papers  (2025-01-25T02:24:00Z)
• 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)
• Embodied Agent Interface: Benchmarking LLMs for Embodied Decision Making [85.24399869971236]
  We aim to evaluate Large Language Models (LLMs) for embodied decision making. Existing evaluations tend to rely solely on a final success rate. We propose a generalized interface (Embodied Agent Interface) that supports the formalization of various types of tasks.
  arXiv  Detail & Related papers  (2024-10-09T17:59:00Z)
• SELF-GUIDE: Better Task-Specific Instruction Following via Self-Synthetic Finetuning [70.21358720599821]
  Large language models (LLMs) hold the promise of solving diverse tasks when provided with appropriate natural language prompts. We propose SELF-GUIDE, a multi-stage mechanism in which we synthesize task-specific input-output pairs from the student LLM. We report an absolute improvement of approximately 15% for classification tasks and 18% for generation tasks in the benchmark's metrics.
  arXiv  Detail & Related papers  (2024-07-16T04:41:58Z)
• 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)
• Efficient Prompting for LLM-based Generative Internet of Things [88.84327500311464]
  Large language models (LLMs) have demonstrated remarkable capacities on various tasks, and integrating the capacities of LLMs into the Internet of Things (IoT) applications has drawn much research attention recently. Due to security concerns, many institutions avoid accessing state-of-the-art commercial LLM services, requiring the deployment and utilization of open-source LLMs in a local network setting. We propose a LLM-based Generative IoT (GIoT) system deployed in the local network setting in this study.
  arXiv  Detail & Related papers  (2024-06-14T19:24:00Z)
• 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)
• Automated Commit Message Generation with Large Language Models: An Empirical Study and Beyond [24.151927600694066]
  Commit Message Generation (CMG) approaches aim to automatically generate commit messages based on given code diffs. This paper conducts the first comprehensive experiment to investigate how far we have been in applying Large Language Models (LLMs) to generate high-quality commit messages.
  arXiv  Detail & Related papers  (2024-04-23T08:24:43Z)
• FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning [70.38817963253034]
  This paper first discusses these challenges of federated fine-tuning LLMs, and introduces our package FS-LLM as a main contribution. We provide comprehensive federated parameter-efficient fine-tuning algorithm implementations and versatile programming interfaces for future extension in FL scenarios. We conduct extensive experiments to validate the effectiveness of FS-LLM and benchmark advanced LLMs with state-of-the-art parameter-efficient fine-tuning algorithms in FL settings.
  arXiv  Detail & Related papers  (2023-09-01T09:40:36Z)

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.