LIMA, Less Is More for Alignment , is a LLM which was created by Meta AI by fine-tuning the LLaMa model on only 1000 samples, and achieved competitive results with top large language models such as GPT-4, Bard and Alpaca. In this post, our goal is to explain the research paper by discussing what the […]
LIMA from Meta AI – Less Is More for Alignment of LLMs Read More »
In this post we will explore Shepherd, a new research paper by Meta AI titled Shepherd: A Critic for Language Model Generation. Large language models have become increasingly sophisticated and given a prompt are able to generate a correct, beautiful and meaningful text. However, a remaining concern with large language models is their tendency to
Shepherd: A Critic for Language Model Generation Read More »
Foundational large language models such as GPT-4, GPT-3.5, LLaMA and more are trained on huge corpus of text from the internet, which contains a large amount of offensive information. Therefore, these foundational large language models are capable of generating a great deal of objectionable content. For this reason, there are a lot of efforts to
Universal and Transferable Adversarial LLM Attacks Read More »
In this post we will dive into a research paper by Google DeepMind titled “From Sparse to Soft Mixtures of Experts”. In recent years we see that transformer-based models are getting larger and larger in order to improve their performance. An undesirable consequence is that the computational cost is also getting larger. And here comes
From Sparse to Soft Mixture of Experts Read More »
