Open foundation and fine-tuned chat models

About the paper: Llama 2: open foundation and fine-tuned models

The Llama 2 paper introduces a new series of advanced language models, ranging from 7 to 70 billion parameters, that are competitive with existing chat models and exhibit competencies comparable to some proprietary models, although they do not outperform GPT-4. The development of Llama 2 is meticulously detailed in the paper, highlighting methodologies like pre-training, supervised fine-tuning, and reinforcement learning from human feedback, emphasizing alignment with helpfulness and safety principles. The open access to Llama 2 has enabled numerous open-source contributors to utilize and further the field.

Key takeaways for the audience

1. Recipe for building Large Language Models (LLMs):
   a. Pre-training: The paper outlines the approach for pretraining large language models, which involves training the model on a vast corpus of diverse text data to help it understand and generate human language.
   b. Supervised fine-tuning: After pretraining, the models undergo supervised fine-tuning, where they are trained on a more specific dataset to refine their capabilities and style in certain areas or tasks.
   c. Reinforcement Learning through human feedback: This stage involves human interaction, where the model learns from feedback given by humans to improve its responses, making them more aligned with desired outcomes and values.

2. Addressing safety and steerability of Large Language Models (LLMs):
   a. Safety considerations: The paper emphasizes the importance of embedding safety features in language models to prevent the generation of harmful, biased, or inappropriate content.
   b. Steerability: It discusses the need for models to be steerable, meaning they can be guided or directed to behave in certain ways, especially in critical applications. This involves techniques that ensure the model’s outputs are aligned with user intentions.

About the presenters

Anjineyulu Venkatesan is a data scientist at Reliance Jio, building computer vision models for IPL and Ajio. Previously, he built linear models for marketing analytics at Bridgei2i. He loves distilling analytical ideas to monetize deep research at an iterative way.

Sachin Dharashivkar is the founder of AthenaAgent, a company that creates AI-powered cybersecurity solutions. Before this, he worked as a Reinforcement Learning engineer. In these roles, he developed agents for doing high-volume equity trading at JPMorgan and for playing video games at Unity.

Participants are advised to read the paper before the event. The format will include a brief overview of the paper, followed by conversations about the technical effects of the Llama2 paper and various future research directions.

RSVP and venue

This is an in-person paper reading session. RSVP to be notified about the venue.

About The Fifth Elephant monthly paper discussions

The Fifth Elephant member - Bharat Shetty Barkur - is the curator of the paper discussions.

Bharat has worked across different organizations such as IBM India Software Labs, Aruba Networks, Fybr, Concerto HealthAI, and Airtel Labs. He has worked on products and platforms across diverse verticals such as retail, IoT, chat and voice bots, edtech, and healthcare leveraging AI, Machine Learning, NLP, and software engineering. His interests lie in AI, NLP research, and accessibility.

The goal is for the community to understand popular papers in Generative AI, DL, and ML domains. Bharat and other co-curators seek to put together papers that will benefit the community, and organize reading and learning sessions driven by experts and curious folks in GenerativeAI, Deep Learning, and Machine Learning.

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