BATGPT: A BIDIRECTIONAL AUTOREGESSIVE TALKER FROM GENERATIVE PRE-TRAINED TRANSFORMER

Abstract

“BATGPT: A Bidirectional Autoregressive Talker from Generative Pre-trained Transformer” is a novel conversational AI model that enhances the capability of natural language understanding and generation. By leveraging the bidirectional autoregressive modeling approach, the system builds on the strengths of generative pre-trained transformers (GPT) to deliver coherent, context-aware, and human-like interactions. Unlike traditional autoregressive models, BATGPT incorporates bidirectional context to understand conversational nuances, making it suitable for applications in customer service, virtual assistance, and educational platforms.

Introduction

Conversational AI has seen rapid advancements, driven by models like GPT. While existing autoregressive models excel at generating text based on past context, they often lack bidirectional comprehension, leading to inconsistencies in understanding and response quality.

BATGPT introduces a bidirectional autoregressive architecture that combines the strengths of bidirectional encoding for contextual comprehension with autoregressive decoding for fluent text generation. This model ensures a deeper understanding of user queries and delivers precise, contextually relevant responses, advancing the state-of-the-art in conversational AI.

Existing System

1. Autoregressive Models (e.g., GPT):
   • Generate responses sequentially, focusing on past context.
   • Struggle with understanding future context in bidirectional tasks.
2. Bidirectional Models (e.g., BERT):
   • Excel in understanding the entire context but are less effective in sequential generation.
3. Hybrid Models:
   • Combine aspects of both but are often computationally intensive and lack optimization for real-time conversational tasks.

Proposed System

BATGPT bridges the gap between bidirectional understanding and autoregressive generation by implementing a bidirectional autoregressive transformer that:

• Understands Context: Processes both past and future contexts within a conversation.
• Generates Coherent Responses: Uses autoregressive decoding to produce natural and logical replies.
• Optimizes Efficiency: Reduces computational overhead by selectively applying bidirectional attention during understanding phases.

Methodology

1. Pre-training:
   • Train the model on diverse datasets using bidirectional context for better understanding and autoregressive decoding for response generation.
2. Bidirectional Autoregressive Architecture:
   • Use a transformer-based framework where:
     • Encoding phase captures bidirectional context.
     • Decoding phase generates text sequentially.
3. Fine-tuning:
   • Customize the model for specific domains such as customer service or healthcare by fine-tuning on domain-specific datasets.
4. Evaluation:
   • Test the model using metrics such as BLEU, ROUGE, and perplexity, and perform human evaluations for conversational quality.

Technologies Used

1. Model Framework: Generative Pre-trained Transformer (GPT) architecture.
2. Programming Language: Python.
3. Deep Learning Frameworks: TensorFlow, PyTorch.
4. Pre-training Dataset: OpenAI’s GPT dataset, fine-tuned with custom datasets (e.g., Common Crawl, Wikipedia, Reddit conversations).
5. Hardware: NVIDIA GPUs for training and inference acceleration.
6. Optimization Tools: Adam optimizer with learning rate warm-up and cosine decay.

Key Contributions

• Bidirectional Autoregressive Model: Combines the best of both bidirectional and autoregressive paradigms.
• Enhanced Conversational Quality: Delivers highly coherent and context-aware responses.
• Scalability: Optimized for deployment in large-scale applications.