NLP Application - Building AI Chatbot Using Transformer Models and LangChain

TL;DR

• Build a chatbot using two LLMs on Transformers architectures: BERT by 谷歌 and GPT by OpenAI
• Let it handle the natural language processing (NLP) task of document-centric questions & answering
• Streamline the development workflow using LangChain

Natural Language Processing with Transformers

Transformer architecture excels at natural language processing (NLP) tasks, analyzing relationships between words, capturing sentence context, and handling complex language by leveraging an encoder-decoder structure:

• Encoder: Transforms the input sequence into a representation, capturing the meaning of each word and its relationship to others.
• Decoder: Generates the output sequence based on the encoder's representation and the decoder's past outputs.

Order Matters In Language

Transformer models rely on self-attention mechanisms to identify the most relevant parts of the input sequence and process the entire sequence at once (parallel processing).

This offers significant speed improvements compared to traditional recurrent neural networks (RNNs) but loses track of the inherent positional information of words in the sentence. To address this setback, Transformers incorporate positional encodings, information about the relative order of the words within sequences, into the input embeddings.

Tranformer Models - BERT = Encoder & GPT = Decoder

In this project, we leverage BERT's strengths in contextual understanding to interpret documents and grasp client questions, and GPT's strengths in generating well-formed answers. Both BERT and GPT are LLMs built on the Transformer architecture, but their training objectives lead to distinct specializations:

BERT (Bidirectional Encoder Representations from Transformers):

• Pretrained LLM by Google - trained to understand the context and meaning of words in a sentence
• Leverage masked language modeling, where random words are masked and the model predicts them based on the surrounding context
• Process text bi-directionally, considering a word's left and right context
• Excel at NLP tasks that require a deeper understanding of the meaning and context of words within a sequence?

GPT (Generative Pre-trained Transformer):

• Pretrained LLM by OpenAI - trained to generate text similar to human-written text
• Leverage autoregressive modeling, where the model predicts the next word based only on the words it has already generated
• Process text unidirectionally from left to right
• Excel at creating and continuing sequences of words in natural and creative ways

Technical Steps

We use an open-source framework, LangChain to deploy the models while connecting with external data sources.

1) External Data Access

Extract data from the PDF file and store them in the Chroma database (open-source vector database)

2) Model Configuration

Load and configure the models.

3) Chain Building & Prompt Customization

Customize prompts accordingly, and construct workflows in a chain by combining the models with the Chroma data source.

4) Results

{
'input_documents': [Document(page_content='in memory, aiding in efficient information retr ieval. It should be noted, however, that the', metadata={'page': 8, 'source': './lang_db/sample.pdf', 'start_index': 155})], 
'question': 'What is LLM?', 
'include_run_info': True, 
'return_only_outputs': True, 
'token_max': 12000
}        

Final answer: Instruction: Return an answer based on the following: ___________________________
LLM stands for Large Language Model. These are a type of artificial intelligence (AI) program that are particularly adept at understanding and generating human language.        

Conclusions

Transformers x LangChain = PowerHouse?

Due to its unique approaches, Transformers can offer speed and accuracy in NLP tasks especially dealing with long sentences. In addition, LangChain allows us to streamline development using pre-built components and customize prompts using built-in tools.

Considerations for Transformers

• Training Requirements: Transformers require large datasets and significant computational resources for training. This can be a barrier for some applications.
• Application Suitability: While powerful, Transformers might be an overkill for simpler chatbot applications.
• Explainability: Their complex nature can make it challenging to understand the reasoning behind their responses.

Overall, Transformers x LangChain can be a powerful tool for building advanced NLP applications such as domain-specific chatbots. Langchain acts as a catalyst, streamlining development and enhancing the model performance.

Reference:

Research paper:

Attention Is All You Need

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

Articles:

Comparison Between BERT and GPT-3 Architectures

Foundation Models, Transformers, BERT and GPT

Machine Learning Mastery The Transformer Model

Official documents:

LangChain LangChain documentation / Chroma

Hugging Face model card: Google Bert / OpenAI GPT