Emergent Abilities in Reduced-Scale Generative Language Models
- URL: http://arxiv.org/abs/2404.02204v1
- Date: Tue, 2 Apr 2024 18:00:28 GMT
- Title: Emergent Abilities in Reduced-Scale Generative Language Models
- Authors: Sherin Muckatira, Vijeta Deshpande, Vladislav Lialin, Anna Rumshisky,
- Abstract summary: Large language models can solve new tasks without task-specific fine-tuning.
This ability is considered an emergent ability and is primarily seen in large language models with billions of parameters.
This study investigates if such emergent properties are strictly tied to model size or can be demonstrated by smaller models trained on reduced-scale data.
- Score: 10.51168925267033
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Large language models can solve new tasks without task-specific fine-tuning. This ability, also known as in-context learning (ICL), is considered an emergent ability and is primarily seen in large language models with billions of parameters. This study investigates if such emergent properties are strictly tied to model size or can be demonstrated by smaller models trained on reduced-scale data. To explore this, we simplify pre-training data and pre-train 36 causal language models with parameters varying from 1 million to 165 million parameters. We show that models trained on this simplified pre-training data demonstrate enhanced zero-shot capabilities across various tasks in simplified language, achieving performance comparable to that of pre-trained models six times larger on unrestricted language. This suggests that downscaling the language allows zero-shot learning capabilities to emerge in models with limited size. Additionally, we find that these smaller models pre-trained on simplified data demonstrate a power law relationship between the evaluation loss and the three scaling factors: compute, dataset size, and model size.
Related papers
- Scaling Smart: Accelerating Large Language Model Pre-training with Small Model Initialization [22.90653167145603]
We introduce HyperCloning, a method that can expand the parameters of a pre-trained language model to those of a larger model with increased hidden dimensions.
As a result, the larger model already inherits the predictive power and accuracy of the smaller model before the training starts.
arXiv Detail & Related papers (2024-09-19T16:50:26Z) - Observational Scaling Laws and the Predictability of Language Model Performance [51.2336010244645]
We propose an observational approach that bypasses model training and instead builds scaling laws from 100 publically available models.
We show that several emergent phenomena follow a smooth, sigmoidal behavior and are predictable from small models.
We show how to predict the impact of post-training interventions like Chain-of-Thought and Self-Consistency as language model capabilities continue to improve.
arXiv Detail & Related papers (2024-05-17T17:49:44Z) - Small Language Models are Good Too: An Empirical Study of Zero-Shot Classification [4.4467858321751015]
We benchmark language models from 77M to 40B parameters using different architectures and scoring functions.
Our findings reveal that small models can effectively classify texts, getting on par with or surpassing their larger counterparts.
This research underscores the notion that bigger isn't always better, suggesting that resource-efficient small models may offer viable solutions for specific data classification challenges.
arXiv Detail & Related papers (2024-04-17T07:10:28Z) - Split and Rephrase with Large Language Models [2.499907423888049]
Split and Rephrase (SPRP) task consists in splitting complex sentences into a sequence of shorter grammatical sentences.
We evaluate large language models on the task, showing that they can provide large improvements over the state of the art on the main metrics.
arXiv Detail & Related papers (2023-12-18T10:16:37Z) - Contrastive Alignment of Vision to Language Through Parameter-Efficient
Transfer Learning [60.26952378997713]
Contrastive vision-language models (e.g. CLIP) are created by updating all the parameters of a vision model and language model through contrastive training.
We show that a minimal set of parameter updates ($$7%) can achieve the same performance as full-model training.
We describe a series of experiments: we show that existing knowledge is conserved more strongly in parameter-efficient training.
arXiv Detail & Related papers (2023-03-21T14:12:08Z) - Go-tuning: Improving Zero-shot Learning Abilities of Smaller Language
Models [23.818751895205132]
Go-tuning is a geometry-guided self-supervised learning method.
Go-tuning can enable T5-small (80M) competitive zero-shot results compared with large language models, such as T5-XL (3B)
arXiv Detail & Related papers (2022-12-20T17:36:49Z) - Training Trajectories of Language Models Across Scales [99.38721327771208]
Scaling up language models has led to unprecedented performance gains.
How do language models of different sizes learn during pre-training?
Why do larger language models demonstrate more desirable behaviors?
arXiv Detail & Related papers (2022-12-19T19:16:29Z) - Emergent Abilities of Large Language Models [172.08007363384218]
We consider an ability to be emergent if it is not present in smaller models but is present in larger models.
The existence of such emergence implies that additional scaling could further expand the range of capabilities of language models.
arXiv Detail & Related papers (2022-06-15T17:32:01Z) - GLaM: Efficient Scaling of Language Models with Mixture-of-Experts [84.33607245023049]
We propose and develop a family of language models named GLaM (Generalist Language Model)
GLaM uses a sparsely activated mixture-of-experts architecture to scale the model capacity while also incurring substantially less training cost compared to dense variants.
It consumes only 1/3 of the energy used to train GPT-3 and requires half of the flops for inference, while still achieving better overall zero-shot and one-shot performance across 29 NLP tasks.
arXiv Detail & Related papers (2021-12-13T18:58:19Z) - Scaling Language Models: Methods, Analysis & Insights from Training
Gopher [83.98181046650664]
We present an analysis of Transformer-based language model performance across a wide range of model scales.
Gains from scale are largest in areas such as reading comprehension, fact-checking, and the identification of toxic language.
We discuss the application of language models to AI safety and the mitigation of downstream harms.
arXiv Detail & Related papers (2021-12-08T19:41:47Z)
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.