AutoMix: Automatically Mixing Language Models

• URL: http://arxiv.org/abs/2310.12963v4
• Date: Fri, 28 Jun 2024 17:57:05 GMT
• Title: AutoMix: Automatically Mixing Language Models
• Authors: Pranjal Aggarwal, Aman Madaan, Ankit Anand, Srividya Pranavi Potharaju, Swaroop Mishra, Pei Zhou, Aditya Gupta, Dheeraj Rajagopal, Karthik Kappaganthu, Yiming Yang, Shyam Upadhyay, Manaal Faruqui, Mausam,
• Abstract summary: Large language models (LLMs) are now available from cloud API providers in various sizes and configurations. We present Automix, an approach that strategically routes queries to larger LMs, based on the approximate correctness of outputs from a smaller LM.
• Score: 62.51238143437967
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) are now available from cloud API providers in various sizes and configurations. While this diversity offers a broad spectrum of choices, effectively leveraging the options to optimize computational cost and performance remains challenging. In this work, we present Automix, an approach that strategically routes queries to larger LMs, based on the approximate correctness of outputs from a smaller LM. Central to Automix are two key technical contributions. First, it has a few-shot self-verification mechanism, which estimates the reliability of its own outputs without requiring extensive training. Second, given that self-verification can be noisy, it employs a POMDP based router that can effectively select an appropriately sized model, based on answer confidence. Experiments across five language models and five challenging datasets show that Automix consistently surpasses strong baselines, reducing computational cost by over 50% for comparable performance.

Related papers

• AutoXPCR: Automated Multi-Objective Model Selection for Time Series Forecasting [1.0515439489916734]
  We propose AutoXPCR - a novel method for automated and explainable multi-objective model selection. Our approach leverages meta-learning to estimate any model's performance along PCR criteria, which encompass (P)redictive error, (C)omplexity, and (R)esource demand. Our method clearly outperforms other model selection approaches - on average, it only requires 20% of computation costs for recommending models with 90% of the best-possible quality.
  arXiv  Detail & Related papers  (2023-12-20T14:04:57Z)
• When Parameter-efficient Tuning Meets General-purpose Vision-language Models [65.19127815275307]
  PETAL revolutionizes the training process by requiring only 0.5% of the total parameters, achieved through a unique mode approximation technique. Our experiments reveal that PETAL not only outperforms current state-of-the-art methods in most scenarios but also surpasses full fine-tuning models in effectiveness.
  arXiv  Detail & Related papers  (2023-12-16T17:13:08Z)
• MatFormer: Nested Transformer for Elastic Inference [94.1789252941718]
  MatFormer is a nested Transformer architecture designed to offer elasticity in a variety of deployment constraints. We show that a 2.6B decoder-only MatFormer language model (MatLM) allows us to extract smaller models spanning from 1.5B to 2.6B. We also observe that smaller encoders extracted from a universal MatFormer-based ViT (MatViT) encoder preserve the metric-space structure for adaptive large-scale retrieval.
  arXiv  Detail & Related papers  (2023-10-11T17:57:14Z)
• Big model only for hard audios: Sample dependent Whisper model selection for efficient inferences [7.592727209806414]
  Several ASR models exist in various sizes, with different inference costs leading to different performance levels. We propose to train a decision module, that would allow, given an audio sample, to use the smallest sufficient model leading to a good transcription. By keeping the decision process computationally efficient, we build a decision module that allows substantial computational savings with reduced performance drops.
  arXiv  Detail & Related papers  (2023-09-22T08:50:58Z)
• AutoML-GPT: Large Language Model for AutoML [5.9145212342776805]
  We have established a framework called AutoML-GPT that integrates a comprehensive set of tools and libraries. Through a conversational interface, users can specify their requirements, constraints, and evaluation metrics. We have demonstrated that AutoML-GPT significantly reduces the time and effort required for machine learning tasks.
  arXiv  Detail & Related papers  (2023-09-03T09:39:49Z)
• Cheaply Evaluating Inference Efficiency Metrics for Autoregressive Transformer APIs [66.30706841821123]
  Large language models (LLMs) power many state-of-the-art systems in natural language processing. LLMs are extremely computationally expensive, even at inference time. We propose a new metric for comparing inference efficiency across models.
  arXiv  Detail & Related papers  (2023-05-03T21:51:42Z)
• eP-ALM: Efficient Perceptual Augmentation of Language Models [70.47962271121389]
  We propose to direct effort to efficient adaptations of existing models, and propose to augment Language Models with perception. Existing approaches for adapting pretrained models for vision-language tasks still rely on several key components that hinder their efficiency. We show that by freezing more than 99% of total parameters, training only one linear projection layer, and prepending only one trainable token, our approach (dubbed eP-ALM) significantly outperforms other baselines on VQA and Captioning.
  arXiv  Detail & Related papers  (2023-03-20T19:20:34Z)
• Robusta: Robust AutoML for Feature Selection via Reinforcement Learning [24.24652530951966]
  We propose the first robust AutoML framework, Robusta--based on reinforcement learning (RL) We show that the framework is able to improve the model robustness by up to 22% while maintaining competitive accuracy on benign samples.
  arXiv  Detail & Related papers  (2021-01-15T03:12:29Z)
• AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction [75.16836697734995]
  We propose a two-stage algorithm called Automatic Feature Interaction Selection (AutoFIS) AutoFIS can automatically identify important feature interactions for factorization models with computational cost just equivalent to training the target model to convergence. AutoFIS has been deployed onto the training platform of Huawei App Store recommendation service.
  arXiv  Detail & Related papers  (2020-03-25T06:53:54Z)

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.