What is the Role of Small Models in the LLM Era: A Survey
- URL: http://arxiv.org/abs/2409.06857v3
- Date: Mon, 30 Sep 2024 10:43:53 GMT
- Title: What is the Role of Small Models in the LLM Era: A Survey
- Authors: Lihu Chen, Gaƫl Varoquaux,
- Abstract summary: Large Language Models (LLMs) have made significant progress in advancing artificial general intelligence (AGI), leading to the development of increasingly large models such as GPT-4 and LLaMA-405B.
scaling up model sizes results in exponentially higher computational costs and energy consumption, making these models impractical for academic researchers and businesses with limited resources.
At the same time, Small Models (SMs) are frequently used in practical settings, although their significance is currently underestimated.
- Score: 13.195074492564332
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Large Language Models (LLMs) have made significant progress in advancing artificial general intelligence (AGI), leading to the development of increasingly large models such as GPT-4 and LLaMA-405B. However, scaling up model sizes results in exponentially higher computational costs and energy consumption, making these models impractical for academic researchers and businesses with limited resources. At the same time, Small Models (SMs) are frequently used in practical settings, although their significance is currently underestimated. This raises important questions about the role of small models in the era of LLMs, a topic that has received limited attention in prior research. In this work, we systematically examine the relationship between LLMs and SMs from two key perspectives: Collaboration and Competition. We hope this survey provides valuable insights for practitioners, fostering a deeper understanding of the contribution of small models and promoting more efficient use of computational resources. The code is available at https://github.com/tigerchen52/role_of_small_models
Related papers
- LLAVADI: What Matters For Multimodal Large Language Models Distillation [77.73964744238519]
In this work, we do not propose a new efficient model structure or train small-scale MLLMs from scratch.
Our studies involve training strategies, model choices, and distillation algorithms in the knowledge distillation process.
By evaluating different benchmarks and proper strategy, even a 2.7B small-scale model can perform on par with larger models with 7B or 13B parameters.
arXiv Detail & Related papers (2024-07-28T06:10:47Z) - Large Language Model Pruning [0.0]
We suggest a model pruning technique specifically focused on LLMs.
The proposed methodology emphasizes the explainability of deep learning models.
We also explore the difference between pruning on large-scale models vs. pruning on small-scale models.
arXiv Detail & Related papers (2024-05-24T18:22:15Z) - MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies [85.57899012821211]
Small Language Models (SLMs) are a resource-efficient alternative to Large Language Models (LLMs)
We introduce MiniCPM, specifically the 1.2B and 2.4B non-embedding parameter variants.
We also introduce MiniCPM family, including MiniCPM-DPO, MiniCPM-MoE and MiniCPM-128K.
arXiv Detail & Related papers (2024-04-09T15:36:50Z) - CogBench: a large language model walks into a psychology lab [12.981407327149679]
This paper introduces CogBench, a benchmark that includes ten behavioral metrics derived from seven cognitive psychology experiments.
We apply CogBench to 35 large language models (LLMs) and analyze this data using statistical multilevel modeling techniques.
We find that open-source models are less risk-prone than proprietary models and that fine-tuning on code does not necessarily enhance LLMs' behavior.
arXiv Detail & Related papers (2024-02-28T10:43:54Z) - Retrieval-based Knowledge Transfer: An Effective Approach for Extreme
Large Language Model Compression [64.07696663255155]
Large-scale pre-trained language models (LLMs) have demonstrated exceptional performance in various natural language processing (NLP) tasks.
However, the massive size of these models poses huge challenges for their deployment in real-world applications.
We introduce a novel compression paradigm called Retrieval-based Knowledge Transfer (RetriKT) which effectively transfers the knowledge of LLMs to extremely small-scale models.
arXiv Detail & Related papers (2023-10-24T07:58:20Z) - A Survey of Large Language Models [81.06947636926638]
Language modeling has been widely studied for language understanding and generation in the past two decades.
Recently, pre-trained language models (PLMs) have been proposed by pre-training Transformer models over large-scale corpora.
To discriminate the difference in parameter scale, the research community has coined the term large language models (LLM) for the PLMs of significant size.
arXiv Detail & Related papers (2023-03-31T17:28:46Z) - Scaling Vision-Language Models with Sparse Mixture of Experts [128.0882767889029]
We show that mixture-of-experts (MoE) techniques can achieve state-of-the-art performance on a range of benchmarks over dense models of equivalent computational cost.
Our research offers valuable insights into stabilizing the training of MoE models, understanding the impact of MoE on model interpretability, and balancing the trade-offs between compute performance when scaling vision-language models.
arXiv Detail & Related papers (2023-03-13T16:00:31Z) - Large Language Models with Controllable Working Memory [64.71038763708161]
Large language models (LLMs) have led to a series of breakthroughs in natural language processing (NLP)
What further sets these models apart is the massive amounts of world knowledge they internalize during pretraining.
How the model's world knowledge interacts with the factual information presented in the context remains under explored.
arXiv Detail & Related papers (2022-11-09T18:58:29Z) - Legal-Tech Open Diaries: Lesson learned on how to develop and deploy
light-weight models in the era of humongous Language Models [10.086015702323971]
We follow the steps of the R&D group of a modern legal-tech start-up and present important insights on model development and deployment.
We start from ground zero by pre-training multiple domain-specific multi-lingual LMs which are a better fit to contractual and regulatory text.
We present benchmark results of such models in a half-public half-private legal benchmark comprising 5 downstream tasks showing the impact of larger model size.
arXiv Detail & Related papers (2022-10-24T10:08:59Z)
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.