THE FRANKLIN

The Science Magazine of Notting Hill & Ealing High School ◆ Spring 2023

Can Chatbots Be Trusted?

By Adeline Goh, 9G

ChatGPT and other chatbots have triggered a huge explosion in the news, with a lot of concern about taking jobs, over-reliance on machines and much more But how do they actually work? Are they really artificial intelligence? What separates a chatbot from a human? continued

^ ChatGPT can be asked questions about almost anything.

To start, we must understand what they are and how they work ChatGPT and other chatbots are all LLMs Also known as Large Language Models, these are essentially just mathematical models; they are probability distributions over sequences of words They all work via Natural Language Processing, or NLP, which is analysis of natural speech, generally to produce suitable responses Some common examples of LLMs include speech recognition (like with Alexa), OCR or Optical Character Recognition (transforming images of text into machine encoded text) and information retrieval (Google Search, etc.).

They work through this statistical distribution of words in human-generated text When given a prompt, eg “The capital of France is”, instead of just ‘knowing’ the answer as a human would, it instead finds the words most likely to follow the prompt In this case, the most statistically probable answer would be “Paris” from sources in its database

^ Using the information from its database, ChatGPT is able to accurately answer the question

In order to learn the structure and relationships between words, LLMs require large datasets; this helps generate a more accurate result since it is so heavily based on probability In aid to statistical analysis, LLMs have a lot of different algorithms to make this work

This puts a huge distinction between humans and LLMs; comparing the thought process of a human to the algorithm of an LLM really illustrates the difference and how far off we are from the general idea of ‘Artificial Intelligence’ and sentient robots Back to the example I initially used, if a human were to be asked “What is the capital of France?”, they would consider various things. They would understand that the question came from another person, and that this person would hear their answer and it could have an effect on their beliefs (all of which an LLM cannot) This is communicative intent, where the person is aware of the impact on the person who asked a question; it is something that LLMs do not possess

On the other hand, LLMs are not inherently conversational; yes, they can generate appropriate responses conforming to conversation-like behaviour, but this dialogue is only facilitated by probability. Part of LLMs generating answers is through prefixes, or prompts, that can generate a relevant response If it were to be responding to the question “What is the closest star to Earth?”, it could generate the prompt “The closest star to the Sun is” followed by the most statistically probable answer, which would be “Proxima Centauri”. LLMs do not ‘know’ things as humans do; they completely lack the concepts of true and false and their answers to questions will always be based solely off of probability.

This presents potential for several different problems

Due to the fact that they work off the data in their datasets, they are prone to bias depending on the information in these The most statistically probable answer will be the most frequently occurring, so if the data were to be biased in some way it would impact the responses of the chatbot This also provides potential for the production of false information presenting as factual Sucincidents are known as hallucinations Contradictory statements are also likely to occur

There is also another huge problem for users, that being the lack of transparency in results The user cannot determine how the chatbot came to its conclusion (like looking at multiple sources on Google), so that puts a huge question on its reliability.

While chatbots do not fit our current perception of artificial intelligence and are prone to several different errors, it is important to remember what a remarkable advancement they are, created from a relatively simple concept They are undoubtedly a step towards actual AI; however, we still have a long way to go before we create a truly intelligent machine capable of thinking and knowing as a human does

^ Here, ChatGPT makes a mistake It works probabilistically rather than deterministically (meaning it can’t actually calculate the answer and instead must rely on information in its database) so it is prone to mistakes in mathematical calculations

References: hitechnectarcom/blogs/here-are-the-top-nlp-language-m odels-that-you-need-to-know/ vitalflux com/large-language-models-concepts-examples/ ?utm content=cmp-true

Talking about Large Language Models - Murray Shanahan, Imperial College

^ The actual answer to the first part of the question, solved by an actual calculator