Reimagining Language Models with Minimalist Approach

The recent surge in interest for smaller language models is a testament to the idea that size isn't everything when it comes to intelligence. Models today are often filled with a plethora of information, but what if we minimized this to create a model that only understands and writes in a single language, yet knows little about the world? This concept is the foundation of the new wave of "tiny" language models.

A novel experiment aimed to emulate the vocabulary of a 3-4 year-old child, using about 1500 basic words. The results were impressive, proving that a smaller model can indeed learn well from a diverse dataset generated by a larger model like GPT-4. This approach leads us to rethink the relevance of conscious thought and intelligence in language understanding and text generation.

What's interesting is that these tiny models, with their limitations, could potentially be as productive as larger generative models. Trained on a specific corpus, say mathematics, they could feasibly generate theorems, introducing a new landscape of possibilities. However, restrictions on using OpenAI's models to generate datasets for competing models could potentially limit this research.

As we move forward, it's plausible to envisage a future where we use larger agents to build smaller ones, effectively automating the process. This research opens doors for those who cannot afford large language model training costs, encouraging more small-scale research.

While it's clear that there's no substitute for a human's ability to understand and generate language, these tiny models provide compelling evidence that perhaps language comprehension is just a complex calculation, something that computers are capable of performing. This could mark the beginning of a new era in AI development and research.

Tags: Language Models, AI Research, Tiny Language Models, OpenAI, Hugging Face, Natural Language Processing

Building Language Models for Low-Resource Languages

As the capabilities of language models continue to advance, it is conceivable that "one-size-fits-all" model will remain as the main paradigm. For instance, given the vast number of languages worldwide, many of which are low-resource, the prevalent practice is to pretrain a single model on multiple languages. In this paper, the researchers introduce the Sabiá: Portuguese Large Language Models and demonstrate that monolingual pretraining on the target language significantly improves models already extensively trained on diverse corpora. Few-shot evaluations … click here to read

Navigating Language Models: A Practical Overview of Recommendations and Community Insights

Language models play a pivotal role in various applications, and the recent advancements in models like Falcon-7B, Mistral-7B, and Zephyr-7B are transforming the landscape of natural language processing. In this guide, we'll delve into some noteworthy models and their applications.

Model Recommendations

When it comes to specific applications, the choice of a language model can make a significant difference. Here are … click here to read

Local Language Models: A User Perspective

Many users are exploring Local Language Models (LLMs) not because they outperform ChatGPT/GPT4, but to learn about the technology, understand its workings, and personalize its capabilities and features. Users have been able to run several models, learn about tokenizers and embeddings , and experiment with vector databases . They value the freedom and control over the information they seek, without ideological or ethical restrictions imposed by Big Tech. … click here to read

Re-Pre-Training Language Models for Low-Resource Languages

Language models are initially pre-trained on a huge corpus of mostly-unfiltered text in the target languages, then they are made into ChatLLMs by fine-tuning on a prompt dataset. The pre-training is the most expensive part by far, and if existing LLMs can't do basic sentences in your language, then one needs to start from that point by finding/scraping/making a huge dataset. One can exhaustively go through every available LLM and check its language abilities before investing in re-pre-training. There are surprisingly many of them … click here to read

Extending Context Size in Language Models

Language models have revolutionized the way we interact with artificial intelligence systems. However, one of the challenges faced is the limited context size that affects the model's understanding and response capabilities.

In the realm of natural language processing, attention matrices play a crucial role in determining the influence of each token within a given context. This cross-correlation matrix, often represented as an NxN matrix, affects the overall model size and performance.

One possible approach to overcome the context size limitation … click here to read

Transforming LLMs with Externalized World Knowledge

The concept of externalizing world knowledge to make language models more efficient has been gaining traction in the field of AI. Current LLMs are equipped with enormous amounts of data, but not all of it is useful or relevant. Therefore, it is important to offload the "facts" and allow LLMs to focus on language and reasoning skills. One potential solution is to use a vector database to store world knowledge.

However, some have questioned the feasibility of this approach, as it may … click here to read

Model Benchmarking: Unveiling Insights into Language Models

Recently, the language model community has been buzzing with discussions about the performance of various models. A particular model that caught our attention is Beyonder , which, in casual testing, seems to be one of the rare non-broken Mixture of Experts (MoEs). It incorporates openchat-3.5 , a model previously benchmarked by the community.

But what's the best inference engine? This question often arises, and it's crucial to consider the source code … click here to read

OpenAI's Language Model - GPT-3.5

OpenAI's GPT-3.5 language model, based on the GPT-3 architecture, is a powerful tool that is capable of generating responses in a human-like manner. However, it still has limitations, as it may struggle to solve complex problems and may produce incorrect responses for non-humanity subjects. Although it is an exciting technology, most people are still using it for 0shot, and it seems unlikely that the introduction of the 32k token model will significantly change this trend. While some users are excited about the potential of the … click here to read

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