Herd: Using multiple, smaller LLMs to match the performances of proprietary, large LLMs via an intelligent composer

• URL: http://arxiv.org/abs/2310.19902v2
• Date: Sat, 21 Sep 2024 07:21:09 GMT
• Title: Herd: Using multiple, smaller LLMs to match the performances of proprietary, large LLMs via an intelligent composer
• Authors: Surya Narayanan Hari, Rex Liu, Matt Thomson,
• Abstract summary: We show that a herd of open source models can match or exceed the performance of proprietary models via an intelligent router. In cases where GPT is not able to answer the query, Herd is able to identify a model that can, at least 40% of the time.
• Score: 1.3108652488669732
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Currently, over a thousand LLMs exist that are multi-purpose and are capable of performing real world tasks, including Q&A, text summarization, content generation, etc. However, accessibility, scale and reliability of free models prevents them from being widely deployed in everyday use cases. To address the first two issues of access and scale, organisations such as HuggingFace have created model repositories where users have uploaded model weights and quantized versions of models trained using different paradigms, as well as model cards describing their training process. While some models report performance on commonly used benchmarks, not all do, and interpreting the real world impact of trading off performance on a benchmark for model deployment cost, is unclear. Here, we show that a herd of open source models can match or exceed the performance of proprietary models via an intelligent router. We show that a Herd of open source models is able to match the accuracy of ChatGPT, despite being composed of models that are effectively 2.5x smaller. We show that in cases where GPT is not able to answer the query, Herd is able to identify a model that can, at least 40% of the time.

Related papers

• EmbedLLM: Learning Compact Representations of Large Language Models [28.49433308281983]
  We propose EmbedLLM, a framework designed to learn compact vector representations of Large Language Models. We introduce an encoder-decoder approach for learning such embeddings, along with a systematic framework to evaluate their effectiveness. Empirical results show that EmbedLLM outperforms prior methods in model routing both in accuracy and latency.
  arXiv  Detail & Related papers  (2024-10-03T05:43:24Z)
• Enabling Small Models for Zero-Shot Classification through Model Label Learning [50.68074833512999]
  We introduce a novel paradigm, Model Label Learning (MLL), which bridges the gap between models and their functionalities. Experiments on seven real-world datasets validate the effectiveness and efficiency of MLL.
  arXiv  Detail & Related papers  (2024-08-21T09:08:26Z)
• EMR-Merging: Tuning-Free High-Performance Model Merging [55.03509900949149]
  We show that Elect, Mask & Rescale-Merging (EMR-Merging) shows outstanding performance compared to existing merging methods. EMR-Merging is tuning-free, thus requiring no data availability or any additional training while showing impressive performance.
  arXiv  Detail & Related papers  (2024-05-23T05:25:45Z)
• When Do We Not Need Larger Vision Models? [55.957626371697785]
  Scaling up the size of vision models has been the de facto standard to obtain more powerful visual representations. We demonstrate the power of Scaling on Scales (S$2$), whereby a pre-trained and frozen smaller vision model can outperform larger models. We release a Python package that can apply S$2$ on any vision model with one line of code.
  arXiv  Detail & Related papers  (2024-03-19T17:58:39Z)
• ModelGPT: Unleashing LLM's Capabilities for Tailored Model Generation [35.160964210941955]
  We propose ModelGPT, a framework designed to determine and generate AI models tailored to the data or task descriptions provided by the user. Given user requirements, ModelGPT is able to provide tailored models at most 270x faster than the previous paradigms.
  arXiv  Detail & Related papers  (2024-02-18T11:24:34Z)
• Adapting Large Language Models for Content Moderation: Pitfalls in Data Engineering and Supervised Fine-tuning [79.53130089003986]
  Large Language Models (LLMs) have become a feasible solution for handling tasks in various domains. In this paper, we introduce how to fine-tune a LLM model that can be privately deployed for content moderation.
  arXiv  Detail & Related papers  (2023-10-05T09:09:44Z)
• Prompt2Model: Generating Deployable Models from Natural Language Instructions [74.19816829003729]
  Large language models (LLMs) enable system builders to create competent NLP systems through prompting. In other ways, LLMs are a step backward from traditional special-purpose NLP models. We propose Prompt2Model, a general-purpose method that takes a natural language task description like the prompts provided to LLMs.
  arXiv  Detail & Related papers  (2023-08-23T17:28:21Z)
• UnIVAL: Unified Model for Image, Video, Audio and Language Tasks [105.77733287326308]
  UnIVAL model goes beyond two modalities and unifies text, images, video, and audio into a single model. Our model is efficiently pretrained on many tasks, based on task balancing and multimodal curriculum learning. Thanks to the unified model, we propose a novel study on multimodal model merging via weight generalization.
  arXiv  Detail & Related papers  (2023-07-30T09:48:36Z)
• "Medium" LMs of Code in the Era of LLMs: Lessons From StackOverflow [5.036273913335737]
  We train two models: SOBertBase, with 109M parameters, and SOBertLarge with 762M parameters, at a budget of just $$187$ and $$800$ each. Results demonstrate that pre-training both extensively and properly on in-domain data can yield a powerful and affordable alternative to leveraging closed-source general-purpose models.
  arXiv  Detail & Related papers  (2023-06-05T21:38:30Z)
• Scalable Performance Analysis for Vision-Language Models [26.45624201546282]
  Joint vision-language models have shown great performance over a diverse set of tasks. Our paper introduces a more scalable solution that relies on already annotated benchmarks. We confirm previous findings that CLIP behaves like a bag of words model and performs better with nouns and verbs.
  arXiv  Detail & Related papers  (2023-05-30T06:40:08Z)

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.