ChatGPT and Large Language Models: Six Evolutionary Steps (2023)

By Dan Philps, PhD, CFA and Tillman Weyde, PhD

Posted In: Drivers of Value, Economics, Future States

The evolution of language models is nothing less than a super-charged industrial revolution. Google lit the spark in 2017 with the development of transformer models, which enable language models to focus on, or attend to, key elements in a passage of text. The next breakthrough — language model pre-training, orself-supervised learning — came in 2020 after which LLMs could be significantly scaled up to drive Generative Pretrained Transformer 3 (GPT-3).

While large language models (LLMs) like ChatGPT are far from perfect, their development will only accelerate in the months and years ahead. The rapid expansion of the ChatGPT plugin store hints at the rate of acceleration. To anticipate how they will shape the investment industry, we need to understand their origins and their path thus far.

So what were the six critical stages of LLMs’ early evolution?

The Business of GPT-4: How We Got Here

ChatGPT and GPT-4 are just two of the many LLMs that OpenAI, Google, Meta, and other organizations have developed. They are neither the largest nor the best. For instance, we prefer LaMDA for LLM dialogue, Google’s Pathways Language Model 2 (PaLM 2) for reasoning, and Bloom as an open-source, multilingual LLM. (The LLM leaderboard is fluid, but this site onGitHubmaintains a helpful overview of model, papers, and rankings.)

So, why has ChatGPT become the face of LLMs? In part, because it launched with greater fanfare first. Google and Meta each hesitated to launch their LLMs, concerned about potential reputational damage if they produced offensive or dangerous content. Google also feared its LLM might cannibalize its search business. But once ChatGPT launched, Google’s CEO Sundar Pichai, reportedly declared a “code red,” and Google soon unveiled its own LLM.

GPT: The Big Guy or the Smart Guy?

The ChatGPT and ChatGPT Plus chatbots sit on top of GPT-3 and GPT-4 neural networks, respectively. In terms of model size, Google’s PaLM 2, NVIDIA’s Megatron-Turing Natural Language Generation (MT-NLG), and now GPT-4 have eclipsed GPT-3 and its variant GPT-3.5, which is the basis of ChatGPT. Compared to its predecessors, GPT-4 produces smoother text of better linguistic quality, interprets more accurately, and, in a subtle but significant advance over GPT-3.5, can handle much larger input prompts. These improvements are the result of training and optimization advances — additional “smarts” — and probably the pure brute force of more parameters, but OpenAI does not share technical details about GPT-4.

ChatGPT and Large Language Models: Six Evolutionary Steps (2)

ChatGPT Training: Half Machine, Half Human

ChatGPT is an LLM that is fine-tuned through reinforcement learning, specificallyreinforcement learning from human feedback (RLHF). The process is simple in principle: First humans refine the LLM on which the chatbot is based by categorizing, on a massive scale, the accuracy of the text the LLM produces. These human ratings then train a reward model that automatically ranks answer quality. As the chatbot is fed the same questions, the reward model scores the chatbot’s answers. These scores go back into fine-tuning the chatbot to produce better and better answers throughthe Proximal Policy Optimization (PPO) algorithm.

ChatGPT Training Process

ChatGPT and Large Language Models: Six Evolutionary Steps (3)
(Video) Large Language Models from scratch

The Machine Learning behind ChatGPT and LLMs

LLMs are the latest innovation in natural language processing (NLP). A core concept of NLP are language models that assign probabilities to sequences of words or text — S = (w1,w2, … ,wm) — in the same way that our mobile phones “guess” our next word when we are typing text messages based on the model’s highest probability.

Steps in LLM Evolution

The six evolutionary steps in LLM development, visualized in the chart below, demonstrate how LLMs fit into NLP research.

The LLM Tech (R)Evolution

1.Unigram Models

The unigram assigns each word in the given text a probability. To identify news articles that describe fraud in relation to a company of interest, we might search for “fraud,” “scam,” “fake,” and “deception.” If these words appear in an article more than in regular language, the article is likely discussing fraud. More specifically, we can assign a probability that a piece of text is about. More specifically, we can assign a probability that a piece of text is about fraud by multiplying the probabilities of individual words:

ChatGPT and Large Language Models: Six Evolutionary Steps (5)

In this equation, P(S) denotes the probability of a sentence S, P(wi) reflects the probability of a word wi appearing in a text about fraud, and the product taken over all m words in the sequence, determines the probability that these sentences are associated with fraud.

These word probabilities are based on the relative frequency at which the words occur in our corpus of fraud-related documents, denoted as D, in the text under examination. We express this as P(w) = count(w) / count(D), where count(w) is the frequency that word w appears in D and count(D) is D’s total word count.

A text with more frequent words is more probable, or more typical. While this may work well in a search for phrases like “identify theft,” it would not be as effective for “theft identify” despite both having the same probability. The unigram model thus has a key limitation: It disregards word order.

2.N-Gram Models

“You shall know a word by the company it keeps!” — John Rupert Firth

The n-gram model goes further than the unigram by examining subsequences of several words. So, to identify articles relevant to fraud, we would deploy such bigrams as “financial fraud,” “money laundering,” and “illegal transaction.” For trigrams, we might include “fraudulent investment scheme” and “insurance claim fraud.” Our fourgram might read “allegations of financial misconduct.”

This way we condition the probability of a word on its preceding context, which the n-gram estimates by counting the word sequences in the corpus on which the model was trained.

The formula for this would be:

ChatGPT and Large Language Models: Six Evolutionary Steps (7)
(Video) GPT3: An Even Bigger Language Model - Computerphile

This model is more realistic, giving a higher probability to “identify theft” rather than “theft identify,” for example. However, the counting method has some pitfalls. If a word sequence does not occur in the corpus, its probability will be zero, rendering the entire product as zero.

As the value of the “n” in n-gram increases, the model becomes more precise in its text search. This enhances its ability to identify pertinent themes, but may lead to overly narrow searches.

The chart below shows a simple n-gram textual analysis. In practice, we might remove “stop words” that provide no meaningful information, such as “and,” “in,” “the,” etc., although LLMs do keep them.

Understanding Text Based on N-Grams

UnigramModern-slavery practices including bonded-labor have
been identified in the supply-chain of Company A
BigramsModern-slavery practices including bonded-labor have
been identified in the supply-chain of Company A
TrigramsModern-slavery practices including bonded-labor have
identified in the supply-chain of Company A
FourgramsModern-slavery practices including bonded-labor have
been identified in the supply-chain of Company A

3.Neural Language Models (NLMs)

In NLMs, machine learning and neural networks address some of the shortcomings of unigrams and n-grams. We might train a neural network model N with the context (wi–(n–1), … ,wi–1) as the input and wi as the target in a straightforward manner. There are many clever tricks to improve language models, but fundamentally all that LLMs do is look at a sequence of words and guess which word is next. As such, the models characterize the words and generate text by sampling the next word according to the predicted probabilities.This approach has come to dominate NLP as deep learning has developed over the last 10 years.

ChatGPT and Large Language Models: Six Evolutionary Steps (8)

4. Breakthrough: Self-Supervised Learning

Thanks to the internet, larger and larger datasets of text became available to train increasingly sophisticated neural model architectures. Then two remarkable things happened:

First, words in neural networks became represented by vectors. As the training datasets grow, these vectors arrange themselves according to the syntax and semantics of the words.

Second, simpleself-supervisedtraining of language models turned out to be unexpectedly powerful. Humans no longer had to manually label each sentence or document. Instead, the model learned to predict the next word in the sequence and in the process also gained other capabilities. Researchers realized that pre-trained language models provide great foundations for text classification, sentiment analysis, question answering, and other NLP tasks and that the process became more effective as the size of the model and the training data grew.

This paved the way for sequence-to-sequence models. These include an encoder that converts the input into a vector representation and a decoder that generates output from that vector. These neural sequence-to-sequence models outperformed previous methods and were incorporated into Google Translate in 2016.

5.State-of-the-Art NLP: Transformers

Until 2017, recurrent networks were the most common neural network architecture for language modeling, long short-term memory (LSTM), in particular. The size of LSTM’s context is theoretically infinite. The models were also made bi-directional, so that also all future words were considered as well as past words. In practice, however, the benefits are limited and the recurrent structure makes training more costly and time consuming: It’s hard to parallelize the training on GPUs.For mainly this reason, transformers supplanted LSTMs.

Transformers build on the attention mechanism: The model learns how much weight to attach to words depending on the context. In a recurrent model, the most recent word has the most direct influence on predicting the next word. With attention, all words in the current context are available and the models learn which ones to focus on.

In their aptly titled paper, “Attention is All You Need,” Google researchers introduced Transformer sequence-to-sequence architecture, which has no recurrent connections except that it uses its own output for context when generating text. This makes the training easily parallelizable so that models and training data can be scaled up to previously unheard of sizes. For classification,the Bidirectional Encoder Representations from Transformers (BERT) became the new go-to model. For text generation, the race was now on to scale up.

6. Multimodal Learning

While standard LLMs are trained exclusively on textual data, other models — GPT-4, for example — include images or audio and video. In a financial context, these models could examine chart, images, and videos, from CEO interviews to satellite photography, for potentially investable information, all cross-referenced with news flow and other data sources.

(Video) Let's build GPT: from scratch, in code, spelled out.

Criticism of LLMs

Transformer LLMs can predict words and excel at most benchmarks for NLP tasks, including answering questions and summarization. But they still have clear limitations. They memorize rather than reason and have no causal model of the world beyond the probabilities of words. Noam Chomsky described them as “high tech plagiarism,” and Emily Bender et al. as “stochastic parrots.” Scaling up the models or training them on more text will not address their deficits. Christopher D. Manning and Jacob Browning and Yann LeCun, among other researchers, believe the focus should be on expanding the models’ technology to multimodality, including more structured knowledge.

LLMs have other scientific and philosophical issues. For example, to what extent can neural networks actually learn the nature of the world just from language? The answer could influence how reliable the models become. The economic and environmental costs of LLMs could also be steep. Scaling up has made them expensive to develop and run, which raises questions about their ecological and economic sustainability.

Artificial General Intelligence (AGI) Using LLMs?

Whatever their current limitations, LLMs will continue to evolve. Eventually they will solve tasks far more complex than simple prompt responses. As just one example, LLMs can become “controllers” of other systems and could in principle guide elements of investment research and other activities that are currently human-only domains. Some have described this as “Baby AGI,” and for us it is easily the most exciting area of this technology.

Baby AGI: Controller LLMs

ChatGPT and Large Language Models: Six Evolutionary Steps (10)

The Next Steps in the AI Evolution

ChatGPT and LLMs more generally are powerful systems. But they are only scratching the surface. The next steps in the LLM revolution will be both exciting and terrifying: exciting for the technically minded and terrifying for the Luddites.

LLMs will feature more up-to-the-minute information, increased accuracy, and the ability to decipher cause and effect. They will better replicate human reasoning and decision making.

For high-tech managers, this will constitute an incredible opportunity to cut costs and improve performance. But is the investment industry as a whole ready for such disruptive changes? Probably not.

Luddite or tech savant, if we cannot see how to apply LLMs and ChatGPT to do our jobs better, it is a sure bet that someone else will. Welcome to investing’s new tech arms race!

For further reading on this topic, check outThe Handbook of Artificial Intelligence and Big Data Applications in Investments, by Larry Cao, CFA, fromCFA Institute Research Foundation.

If you liked this post, don’t forget to subscribe to theEnterprising Investor.

All posts are the opinion of the author(s). As such, they should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute or the author’s employer.

Image credit: ©Getty Images / imaginima

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(Video) AI Showdown: ChatGPT vs. OPT vs. BLOOM - Which Language Model Reigns Supreme?

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About the Author(s)

Dan Philps, PhD, CFA

Dan Philps, PhD, CFA, is head of Rothko Investment Strategies and is an artificial intelligence (AI) researcher. He has 20 years of quantitative investment experience. Prior to Rothko, he was a senior portfolio manager at Mondrian Investment Partners. Before 1998, Philps worked at a number of investment banks, specializing in the design and development of trading and risk models. He has a PhD in artificial intelligence and computer science from City, University of London, a BSc (Hons) from King’s College London, is a CFA charterholder, a member of CFA Society of the UK, and is an honorary research fellow at the University of Warwick.

Tillman Weyde, PhD

Tillman Weyde is a reader in the Department of Computer Science at City, University of London and is a veteran artificial intelligence (AI) researcher. He is the head of the Machine Intelligence and the Media Informatics Research Groups at City. Weyde has worked in the field of AI for more than 25 years and is an award-winning AI researcher, with more than 150 major publications. He holds degrees in mathematics, computer science, and music from the University of Osnabrück and gained his PhD in 2002.

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(Video) Reinforcement Learning from Human Feedback: From Zero to chatGPT


Is ChatGPT a large language model? ›

ChatGPT is a large language model (LLM) and artificial intelligence (AI) system designed by OpenAI ( to interact with people in a natural and conversational way [1].

What language model is behind the ChatGPT? ›

ChatGPT is a member of the generative pre-trained transformer (GPT) class of language models. It's a task-specific GPT that was fine-tuned to target conversational usage, and was originally built upon an improved version of OpenAI's GPT-3 model known as "GPT-3.5".

How does a large language model chatbot create its response? ›

To generate these responses, LLMs use a technique called natural language generation (NLG). This involves examining the input and using the patterns learned from its data repository to generate a contextually correct and relevant response.

What are the different types of LLM models? ›

List of large language models
NameRelease dateDeveloper
GPT-NeoMarch 2021EleutherAI
25 more rows

What is ChatGPT and how does it work? ›

ChatGPT is a natural language processing tool driven by AI technology that allows you to have human-like conversations and much more with the chatbot. The language model can answer questions and assist you with tasks, such as composing emails, essays, and code.

Will ChatGPT replace programmers? ›

The short answer is no, ChatGPT will not replace programmers entirely. However, it has the potential to automate some aspects of programming, such as code generation, bug fixing, and documentation. ChatGPT can learn from vast amounts of code and data, making it possible to generate new code similar to existing code.

How do large language models like ChatGPT work? ›

An LLM Uses a Transformer Architecture

More specifically, LLMs use a particular neural network architecture called a transformer, which is designed to process and generate data in sequence, like text. An architecture in this context describes how the neurons are connected to one another.

How many layers does ChatGPT have? ›

The original GPT model had 12 layers, but subsequent versions, such as GPT-2 and GPT-3, have many more layers and let it uses up to 175 billion parameters in GPT-3 that is currently one of the largest neural networks ever created.

How does ChatGPT generate responses? ›

To generate responses, ChatGPT uses a multi-layer transformer network, which is a type of deep learning architecture that has proven to be effective at processing natural language. The model takes an input sentence, processes it using its internal knowledge, and then generates a response that is relevant to the input.

What is the purpose of large language models? ›

A large language model (LLM) is a type of artificial intelligence (AI) algorithm that uses deep learning techniques and massively large data sets to understand, summarize, generate and predict new content.

Where does ChatGPT get its data? ›

ChatGPT is an AI language model that was trained on a large body of text from a variety of sources (e.g., Wikipedia, books, news articles, scientific journals).

What are the advantages of large language models? ›

One of the key advantages of large language models is their ability to learn from context. They can understand the meaning and intent behind words and phrases, allowing them to generate more accurate and appropriate responses.

Who uses large language models? ›

Search engines can use large language models to provide more direct, human-like answers. Life science researchers can train large language models to understand proteins, molecules, DNA and RNA. Developers can write software and teach robots physical tasks with large language models.

What was the first large language model? ›

History of Large Language Models

One of the earliest examples of a language model was ELIZA, a program developed by Joseph Weizenbaum at MIT in 1966.

What is the difference between NLP and LLM? ›

NLP (Natural Language Processing) is a field of AI focused on understanding and processing human language. LLMs, on the other hand, are specific models used within NLP that excel at language-related tasks, thanks to their large size and ability to generate text.

Is ChatGPT a neural network? ›

ChatGPT is a language model that was created by OpenAI in 2022. Based on neural network architecture, it's designed to process and generate responses for any sequence of characters that make sense, including different spoken languages, programming languages, and mathematical equations.

What are the benefits of ChatGPT? ›

The biggest advantage of ChatGPT is its ability to understand natural language and have a text conversation with people. The system can answer questions about nearly any subject with authority and even understand a customer's intent.

What are the parameters of ChatGPT? ›

What are parameters? Parameters are the settings that dictate how ChatGPT generates text. Think of them as levers you can pull to fine-tune the AI's output, helping you create content that matches your desired style, tone, and creativity.

Which jobs can ChatGPT replace? ›

Jobs At Risk of Being Replaced by ChatGPT
  • Mathematicians.
  • Interpretators and Translators.
  • Poets, Lyricists, and Creative Writers.
  • Web Designers.
  • News Analysts, Reporters, and Journalists.
  • Lawyers and Paralegals.
  • Accountants and Auditors.
  • Teachers.
May 2, 2023

Will ChatGPT replace Google? ›

ChatGPT is not replacing Google. OpenAI's chatbot is not designed to act as a search engine. It functions well as a question-answering chatbot and a personal assistant for a variety of tasks. So, if you were hoping to use ChatGPT to find your local bus schedule you may want to think again.

Will ChatGPT replace data scientists? ›

While AI language models like ChatGPT can generate text and perform certain data analysis tasks, they cannot replace the expertise and creativity of a human data scientist.

Does ChatGPT use machine learning? ›

ChatGPT has been fine-tuned from a model in the GPT-3.5 series. It was trained on diverse data, including books, articles, and conversations, to understand various topics and contexts. It uses machine learning to generate human-like responses to text prompts.

Does ChatGPT learn from users? ›

The answer to that is, yes, ChatGPT learns from user input—but not in the way that most people think. Here's an in-depth guide explaining why ChatGPT tracks conversations, how it uses them, and whether your security is compromised.

Is ChatGPT a language prediction model? ›

Built on a large language model – and a product of so-called generative/conversational AI – this chatbot allows users to have human-like conversations as it can recognize patterns and create new outputs based on its understanding. The app's growth has outpaced the likes of TikTok, Instagram and others.

How much will ChatGPT cost? ›

ChatGPT Plus was announced at the end of January 2023 and in relation to ChatGPT Pro would be one of the lower-cost plans. You can get access to this paid version of ChatGPT for a monthly subscription of $20.

How does ChatGPT write code? ›

Chat GPT is not specifically designed to write code but can assist in the process. Using machine learning algorithms, Chat GPT can analyze and understand code snippets and generate new code based on the input it receives.

Is ChatGPT free to use? ›

Yes, you can use ChatGPT for free -- for now.

Does ChatGPT give the same answer to everyone? ›

No, ChatGPT does not give the exact same answer and wording to everyone who asks the same question. While it may generate similar responses for identical or similar queries, it can also produce different responses based on the specific context, phrasing, and quality of input provided by each user.

Is ChatGPT a deep learning model? ›

1. Deep Learning Architecture: ChatGPT is based on the GPT-3.5 architecture, which uses a deep neural network with hundreds of millions of parameters to analyze and generate text.

How are large language models implemented? ›

The model is trained on the pre-processed text data using supervised learning. During training, the model is presented with a sequence of words and is trained to predict the next word in the sequence. The model adjusts its weights based on the difference between its prediction and the actual next word.

What is the most powerful language model? ›

The Megatron-Turing Natural Language Generation (MT-NLG) model is a transformer-based language model with 530 billion parameters, making it the largest and most powerful of its kind.

Does ChatGPT save your searches? ›

OpenAI, which makes ChatGPT, says it only saves your searches to train and improve its models. It doesn't use chatbot interactions to build profiles of users or advertise, said an OpenAI spokeswoman, who added Thursday that it has no plans to do so in the future.

Can ChatGPT access databases? ›

The retrieval plugin allows ChatGPT to search a vector database of content, and add the best results into the ChatGPT session.

What are the capabilities of ChatGPT? ›

What can ChatGPT do? ChatGPT can chat, answer questions, create content, write code and debug, take tests, manipulate data, explain and tutor, and more!

What is the power of large language models? ›

Large language models (LLM) like GPT-3 have revolutionized machines' ability to understand human language. Trained on datasets of immense size and complexity, LLMs finally help computers understand and respond to nuanced human conversations and questions.

Why do we need language models? ›

Language models analyze bodies of text data to provide a basis for their word predictions. They are used in natural language processing (NLP) applications, particularly ones that generate text as an output. Some of these applications include , machine translation and question answering.

What are the different types of language models in AI? ›

Types of Language Models

There are two primary approaches when it comes to language modeling: Statistical models and neural models. Statistical models, as the name would suggest, focus on using statistics to predict which words are most likely to appear in a given sequence.

What are the top 10 large language models? ›

OWASP Top 10 List for Large Language Models version 0.1
  • LLM01:2023 - Prompt Injections. ...
  • LLM02:2023 - Data Leakage. ...
  • LLM03:2023 - Inadequate Sandboxing. ...
  • LLM04:2023 - Unauthorized Code Execution. ...
  • LLM05:2023 - SSRF Vulnerabilities. ...
  • LLM06:2023 - Overreliance on LLM-generated Content. ...
  • LLM07:2023 - Inadequate AI Alignment.

What are the most famous large language models? ›

The Most Important Large Language Models (LLMs) in 2023
  1. BERT by Google. Summary. ...
  2. GPT-3 by OpenAI. Summary. ...
  3. LaMDA by Google. Summary. ...
  4. PaLM by Google. Summary. ...
  5. LLaMA by Meta AI. Summary. ...
  6. GPT-4 by OpenAI. Summary.
Apr 11, 2023

How accurate are large language models? ›

Publicly available large language models do not provide a degree of confidence for the accuracy of their output. One main challenge is that they are not explicitly designed to provide truthful answers; rather, they are primarily trained to generate text that follows the patterns of human language.

What does ChatGPT stand for? ›

Chat GPT stands for Chat Generative Pre-Trained Transformer and was developed by an AI research company, Open AI. It is an artificial intelligence (AI) chatbot technology that can process our natural human language and generate a response.

What are the models of language evolution? ›

According to this point of view, language evolution models have been classified into three main categories that are: agent-based, evolutionary computation-based, and game theoretic.

What are the three models of language? ›

The goal of a linguist is to discover grammars that are simple and yet are able to fully span the language. In [1] Chomsky describes three possible options of increasing complexity for English grammars: Finite-state, Phrase Structure and Transformational.

What are the two types of NLP? ›

Syntax and semantic analysis are two main techniques used with natural language processing. Syntax is the arrangement of words in a sentence to make grammatical sense. NLP uses syntax to assess meaning from a language based on grammatical rules.

How is NLP different from NLP? ›

Differences between NLP and NLP

NLP is primarily used in computer science to enable machines to understand and interact with human language, while NLP is primarily used in personal development to improve human communication skills and change negative thought patterns.

What are the largest AI language models? ›

The Most Important Large Language Models (LLMs) in 2023
  1. BERT by Google. Summary. ...
  2. GPT-3 by OpenAI. Summary. ...
  3. LaMDA by Google. Summary. ...
  4. PaLM by Google. Summary. ...
  5. LLaMA by Meta AI. Summary. ...
  6. GPT-4 by OpenAI. Summary.
Apr 11, 2023

What is the largest NLP model? ›

The Megatron-Turing Natural Language Generation (MT-NLG) model is a transformer-based language model with 530 billion parameters, making it the largest and most powerful of its kind.

What are some examples of large language models? ›

There are multiple large language models developed. Examples include the GPT-3 and GPT-4 from OpenAI, LLaMA from Meta, and PaLM2 from Google. These are models that can understand language and can generate text.

What is the most advanced AI system in the world? ›

GPT-3 was released in 2020 and is the largest and most powerful AI model to date. It has 175 billion parameters, which is more than ten times larger than its predecessor, GPT-2.

What are the four models of AI? ›

According to the current system of classification, there are four primary AI types: reactive, limited memory, theory of mind, and self-aware.

What are the 7 levels of NLP? ›

There are seven processing levels: phonology, morphology, lexicon, syntactic, semantic, speech, and pragmatic.

Does Google have a large language model? ›

At its I/O developer conference, Google today announced the launch of PaLM 2, its newest large language model (LLM). PaLM 2 will power Google's updated Bard chat tool, the company's competitor to OpenAI's ChatGPT, and function as the foundation model for most of the new AI features the company is announcing today.

What are the drawbacks of ChatGPT? ›

What Are the Limitations of ChatGPT?
  • Incorrect answers.
  • Biased answers.
  • Lack of human insight.
  • Overly long (wordy) answers.
Apr 20, 2023

What are the Disadvantages of the ChatGPT? ›

OpenAI admits that ChatGPT can produce harmful and biased answers, hoping to mitigate the problem by gathering user feedback. But its ability to produce convincing text, even when the facts aren't true, can easily be used by bad actors.

What are the positive and negative effects of ChatGPT? ›

The positive side is that students can now get information and brainstorm any time of the day and regardless of their location. The negative side, however, is that critical thinking and making it easier for students to cheat have been a concern that needs tackling.


1. Stanford Webinar - GPT-3 & Beyond
(Stanford Online)
2. On Large Language Models for Understanding Human Language Christopher Manning
3. Generative AI, Large Language Models, Prompt Engineering, Reinforcement Learning, and Human Feedback
4. LLaMA & Alpaca: “ChatGPT” On Your Local Computer 🤯 | Tutorial
(Martin Thissen)
5. How ChatGPT works | Stephen Wolfram and Lex Fridman
(Lex Clips)
6. Deep Dive into ChatGPT - Architecture & Comparison with Bard, LLaMa
(Applied Singularity)
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