The Role of Natural Language Processing (NLP) in Chatbots

Introduction

Natural Language Processing (NLP) is the process through which a computer understands natural language. The recent progress in NLP forms the foundation of the new generation of generative AI chatbots.

NLP architecture has a multifaceted role in the modern chatbot. It enables these chatbots to understand the natural language prompts you give and helps the chatbot generate words. These new capabilities depend on the transformer model, a novel model developed by Google researchers in 2017.

However, modern chatbots employ complex NLP algorithms to understand texts and images. Let’s decode these processes by looking at the role NLP plays.

Overview:

• NLP’s Role in Modern Chatbots: NLP is central to how chatbots understand and generate responses, relying heavily on transformer models like BERT and GPT for language understanding, multi-turn conversations, and multilingual support.
• Core Components: The current NLP landscape includes models for language understanding (e.g., BERT, GPT), mechanisms for multi-turn conversations, and multilingual support, essential for global business applications.
• Challenges in NLP: Despite advancements, NLP models face limitations in handling colloquial language, spelling/grammar errors, and ethical biases, often leading to inaccuracies or biased outputs.
• Importance and Future Prospects: While NLP technologies are vital to chatbot functionality, ongoing challenges like bias, hallucinations, and error handling need to be addressed for further progress.

Role of NLP in Modern Chatbots

Modern chatbots use vector embeddings to parse text into matrices that can then be used to understand the prompts you gave. The way this works is as follows:

1. Your prompts are tokenized: Tokenization is a machine-learning process that breaks down a large amount of data into smaller chunks. In the case of your prompts, your sentences are broken down into smaller parts.

2. These tokens are then processed using a transformer model: Models like BERT take the prompt and vectorize them using a “self-attention” mechanism.

3. The chatbot compares your input with its own vector space: Computers virtually map out the distance between your prompt and their training data in the vector space to calculate the probability of the next word in your answer.

4. Your answer is generated: The chatbot then answers your prompt.

It’s important to note that while chatbots are extensively fine-tuned to give answers to your questions, the machine learning (ML) operation they’re doing is completion. They’re taking your prompt and trying to predict the next word in the sequence based on the context.

Now that we understand the basic NLP processes in our modern chatbots, let’s understand the current architecture that we use for them.

The Current NLP Landscape

There are three major components of the current NLP landscape. Let’s explore them in turn.

1. Language Understanding

BERT Models: BERT models are bidirectional models that use the idea that your input is connected to the output it will generate. So, after your input text has been vectorized using an encoder, a decoder focuses on different parts of the input and outputs a matching statement. These models use the self-attention mechanism from the paper “Attention is All You Need.”

GPT: GPT is unidirectional and uses the Decoder from the Transformer architecture. This uses masked self-attention, which includes tokens in the attention computation while ignoring the future tokens based on their positions in the sequence.

So, the model pays attention to your prompt and goes till the last word it predicted, and based on that input, it predicts the following word in the sequence.

XLNet and PaLM: While the unidirectional model of GPT could answer many questions by using a large corpus of data, it still lacked the bidirectional context necessary for understanding complex data.

The PaLM models did this by using a unidirectional model while changing the order in which the tokens are read, allowing the model to read tokens depending on dynamic factorization. This makes bidirectional understanding possible in an unidirectional model.

2. Multi-Turn Conversations

Multi-turn conversations are crucial for our modern chatbots. People want to have more extended conversations with ChatGPT and Claude and remember the previous things they have said.

Now, there are two capabilities you need to add to help chatbots make multi-turn conversations possible.

Contextual Understanding

If a user wants to update their initial request as the conversation continues, the chatbot needs to remember the context of the conversation. Modern chatbots do this by taking each request submitted by the user and adding it to structured data to give accurate information, i.e., it takes all the messages from the user to create a unified data structure. We have recently introduced this feature at Kommunicate, and this is how it functions.

Dialog Policies

Sometimes, a user requests a chatbot to do something too specific or enters a prompt that goes outside the business policies of the chatbot. When this happens, the chatbot refers to some internal conversational rules or dialog policies. In business, this often means that the chatbot queries a database and asks clarifying questions from the user until the request matches its business policies.

Multi-turn conversations are at the heart of the Generative AI promise. It allows chatbots to have more extended conversations with users and serve their needs better. It’s also why “context length” has been a veritable buzzword around LLMs for the past few years.

3. Multilingual Support

Since LLMs are being built for the generic business use case, it is essential to incorporate multilingualism. This allows modern chatbots to be deployed for global businesses without additional training for specific localities.

Chatbots answer multilingual questions by the following process:

Changing Prompt to Data: The chatbot takes in the prompt in any language and puts it in a linguistic framework it understands. The core linguistic framework for LLMs is often English, so it translates the prompt into data and parses that data based on the English linguistic framework.

Task-Solving: The chatbot thinks of the answer to the prompt in English while incorporating data from multilingual neurons within the model. LLMs use self-attention and feed-forward mechanisms to get to the answer.

Generating Output: The LLM gets its answer in the form of data arranged in the linguistic framework and then translates it back into the original query language.

Several models, like Cohere’s Aya models, excel at providing multilingual support because they have been trained on expert-curated multilingual datasets with an “over-emphasis on academic-style documents.”

With these three basic capabilities, NLP offers extensive functionality to the recent LLM models. However, the current NLP architecture still has some problems. Let’s explore these limitations next.

Limitations and Challenges in NLP

Despite the rapid evolution of NLP models, there are still some limitations in how they function. These are:

1. Handling Colloquialism

Slang is a natural part of human conversation; however, several LLMs struggle to understand slang terms. For example, “blazing” refers to “something excellent” in the U.S., but it translates to “anger” in the U.K., and most LLMs can’t handle this discrepancy.

The main challenge in handling slang terms is the lack of quality datasets that explain their meanings. Even state-of-the-art models like ChatGPT 4 lack enough data to identify slang terms.

2. Dealing with Spelling and Grammar Errors

While newer models of chatbots can detect errors, they struggle with correcting them. This can mean that the LLM tries to correct an input sequence but changes its meaning, giving you wrong results with its responses.

This can be solved by extensive fine-tuning and heuristics, something that applications like Grammarly and Google Search have done previously in other ML contexts.

3. Ethical Bias and Incorrectness

Hallucinations and AI bias is an ongoing problem. Essentially, since training datasets might have a bias towards certain philosophies, lesser known nuances might be missed.

Plus, if an AI can’t find an answer, it often tries to answer and gives wrong information by hallucinating. These two problems are currently being heavily researched, but, there are no empirical solutions yet.

Conclusion

NLP is central to the functioning of chatbots. It’s used in everything from tokenization and vectorization of your prompts, to giving the answers a user requested.

This is possible because of the current NLP architecture, which uses multiple transformer models to understand language in all its forms. The architecture also supports longer context lengths and multilingual neurons that enables multi-turn and multilingual conversations.

While this progress is significant, there are still multi-layered challenges with NLP tech. Currently, the tech struggles to handle spell checking, grammatical errors and slang terms in its input text. The current NLP tech is still prone to hallucinations and biases.

However, despite these challenges, NLP is critical to the modern chatbot ecosystem, and empowers it to be good at a wide range of tasks.

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