Applying quantum computing principles to large language models for more reliable, interpretable, and steerable systems.
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Updated
Jan 5, 2024 - Python
Applying quantum computing principles to large language models for more reliable, interpretable, and steerable systems.
Survey of preference alignment algorithms
Intelligent AI Chatbot that has the capability to learn from the user
Library built on TextRL for easy training and usage of fine-tuned models using RLHF, a rewards model, and PPO
Code for my thesis titled "Eliciting latent knowledge from language reward models" for the MPhil in Machine Learning and Machine Intelligence at the University of Cambridge
This project is based on fine-tuning LLM models (FLAN-T5) for text summarisation task using PEFT approach. All evaluation metrics being computed on ROUGE scoring and LoRA optimisation techniques being used for fine-tuning.
An alternative RLHF reward model formulation from a social choice perspective
Implemented the Proximal Policy Optimization (PPO) algorithm to fine-tune a large language model for generating consistently positive reviews
Projects and Models built in Python leveraging PyTorch, implementing Reinforcement Learning algorithms for reward-based tasks.
This repository is dedicated to small projects and some theoretical material that I used to get into NLP and LLM in a practical and efficient way.
Improving LLM truthfulness via reporting confidence
Aligning GPT2 model to generate Non-Toxic words
Applying AlphaZero Self-Play Tactics to LLaMA for Enhanced Chatbot Interaction
A Comparison of LLM Chat Bot Implementation Methods with Travel Use Case
Codebase and experiments of LLM(Large Language Modeling)
Summaries of papers related to the alignment problem in NLP
Researching the reinforcement learning algorithm of ChatGPT
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