CSE 587: Deep Learning for Natural
Language Processing
Lecture 8. ChatGPT and Beyond

Rui Zhang
Spring 2023

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What is GPT-3
in its own words:

Can GPT-3 write an academic paper on itself, with minimal human input?
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What is GPT-3

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What is GPT-3

https://beta.openai.com/examples

4What is GPT-3

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What is ChatGPT

https://openai.com/blog/chatgpt/ November 30, 2022

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ChatGPT - Official Example 1

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ChatGPT - Official Example 2
In the following sample, ChatGPT
initially refuses to answer a questionthat could be about illegal activities
but responds after the user clarifies
their intent.

8ChatGPT - Official Example 3
In the following sample, ChatGPT provides
responses to follow-up instructions.

9ChatGPT - Own Experience10ChatGPT - Own Experience11ChatGPT is Scarily Good
ChatGPT could be a Stanford medical student, a lawyer, or a financial analyst.
Here's a list of advanced exams the AI bot has passed so far.
● Wharton MBA exam
● US medical licensing exam
● Law School Exams● Stanford Medical School clinical reasoning final● …

https://www.businessinsider.com/list-here-are-the-exams-chatgpt-has-passed-so-far-2023-1

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Systematic Evaluation of ChatGPT●●●Is ChatGPT a general-purpose NLP task solver?
On what types of tasks does ChatGPT perform well?
If ChatGPT fell behind other models on certain tasks, why?

Is ChatGPT a General-Purpose Natural Language Processing Task Solver? (Qin et al., 2023)

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Systematic Evaluation of ChatGPTKey Takeaways
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For a generalist NLP model: ChatGPT often performs worse than models that are fine-tuned
on a given task.
For Reasoning: ChatGPT is empirically substantiated in arithmetic reasoning; However,
ChatGPT often underperforms GPT-3.5 in commonsense, symbolic, and logical reasoning
tasks.
ChatGPT outperforms GPT-3.5 for natural language inference tasks and question
answering/reading comprehension tasks.
ChatGPT is superior to GPT-3.5 for dialogue tasks.
ChatGPT generates longer summaries and performs worse than GPT-3.5 for summarization
tasks.
Both ChatGPT and GPT-3.5 face challenges on certain tasks such as sequence tagging.
ChatGPT’s sentiment analysis ability comes close to that of GPT-3.5

Is ChatGPT a General-Purpose Natural Language Processing Task Solver? (Qin et al., 2023)

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Systematic Evaluation of ChatGPT
ChatGPT fails to generalize to extremely low-resource languages

A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination,and Interactivity (Bang et al., 2023)

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Systematic Evaluation of ChatGPT
ChatGPT fails to generalize to extremely low-resource languages

A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination,and Interactivity (Bang et al., 2023)16

Systematic Evaluation of ChatGPT
Thanks to the code understanding and generation abilityof ChatGPT, we believe programming codes can serve as
the intermediate medium to bridge vision and language

A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination,and Interactivity (Bang et al., 2023)

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ChatGPT is Still LimitedWhy?
● reporting bias
● lacks common sense and reasoning
over the real physical world

A Categorical Archive of ChatGPT Failures
Failure archive for ChatGPT and similar models

18ChatGPT is Still Limited
hallucinations

Thanks, but this is wrong…

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What is ChatGPT: An Insightful Analogy

Imagine that you’re about to lose your access to the Internet forever. In preparation, you plan to create acompressed copy of all the text on the Web, so that you can store it on a private server.
The only catch is that, because the text has been so highly compressed, you can’t look for information by
searching for an exact quote; you’ll never get an exact match, because the words aren’t what’s being stored. To
solve this problem, you create an interface that accepts queries in the form of questions and responds withanswers that convey the gist of what you have on your server.
What I’ve described sounds a lot like ChatGPT, or most any other large language model. Think of ChatGPT as ablurry jpeg of all the text on the Web. It retains much of the information on the Web, in the same way that a jpeg
retains much of the information of a higher-resolution image, but, if you’re looking for an exact sequence of bits,
you won’t find it; all you will ever get is an approximation. But, because the approximation is presented in the formof grammatical text, which ChatGPT excels at creating, it’s usually acceptable. You’re still looking at a blurry jpeg,
but the blurriness occurs in a way that doesn’t make the picture as a whole look less sharp.

20How does ChatGPT Obtain its Ability?

Predictive Learning, NeurIPS 2016 | Yann LeCun

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How does ChatGPT Obtain its Ability?
●●●Supervised Training
Pretraining and Finetuning
Prompt-based Method○ Template Prompt○ In-Context Learning○ Instruction Finetuning■ Supervised Instruction Finetuning■ Reinforcement Learning from Human Feedback (RLHF)

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In-Context LearningFigure from http://ai.stanford.edu/blog/in-context-learning/
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Instruction Finetuning
From prediction text to following instructions

Multitask Prompted Training Enables Zero-Shot Task Generalization
Finetuned Language Models Are Zero-Shot Learners

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Reinforcement Learning from Human Feedback (RLHF)

Pretraining a language
model

Gathering data and training a
reward model

Fine-tuning the LM withreinforcement learning

Illustrating Reinforcement Learning from Human Feedback (RLHF)
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How does GPT Obtain its Ability?

How does GPT Obtain its Ability? Tracing Emergent Abilities of Language Models to their Sources26

How does GPT Obtain its Ability?
We have concluded:● The language generation ability + basic world knowledge + in-context learning are from pretraining(davinci)● The ability to store a large amount of knowledge is from the 175B scale.
● The ability to follow instructions and generalizing to new tasks are from scaling instruction tuning(davinci-instruct-beta)● The ability to perform complex reasoning is likely to be from training on code (code-davinci-002)● The ability to generate neutral, objective, safe, and informative answers are from alignment withhuman. Specifically:○○○●If supervised tuning, the resulting model is text-davinci-002
If RLHF, the resulting model is text-davinci-003
Either supervised or RLHF, the models cannot outperform code-davinci-002 on many tasks, which is called the alignment tax.

The dialog ability is also from RLHF (ChatGPT), specifically it tradeoffs in-context learning for:○○○Modeling dialog history
Increased informativeness
Rejecting questions outside the model’s knowledge scope

How does GPT Obtain its Ability? Tracing Emergent Abilities of Language Models to their Sources

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The Bitter Lesson
The biggest lesson that can be read from 70 years of AI research is that general
methods that leverage computation are ultimately the most effective, and by alarge margin.
……
This is a big lesson. The bitter lesson is based on the historical observations that
1) AI researchers have often tried to build knowledge into their agents, 2) thisalways helps in the short term, and is personally satisfying to the researcher, but3) in the long run it plateaus and even inhibits further progress, and 4)
breakthrough progress eventually arrives by an opposing approach based on
scaling computation by search and learning.

The Bitter Lesson, Rich Sutton, 201928

Large Language Models are Getting Bigger

Figure Adapted from Myle Ott

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Rumor of GPT-4

GPT-4: Facts, Rumors and Expectations about next-gen AI model

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Scaling Laws of LLMs

Scaling Laws for Neural Language Models (Kaplan et al., 2020)31

Scaling Unlocks Emergent Abilities> A few-shot prompted task is emergent if it
achieves random accuracy for small models
and above-random accuracy for large
models.

Scaling unlocks emergent abilities in language models

32Scaling Unlocks Emergent Abilities

Scaling unlocks emergent abilities in language models

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Emergent Abilities Transcend the Scaling Law

A Closer Look at Large Language Models Emergent Abilities

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Is Scaling of LLMs All We Need?
Inverse Scaling Prize

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Is Scaling of LLMs All We Need?
Inverse Scaling Prize

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Is Scaling of LLMs All We Need?
Inverse Scaling Prize

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Is Scaling of LLMs All We Need?
Inverse Scaling Prize

38Inverse Scaling Can Become U-shaped

Inverse scaling can become U-shaped39

Why did all of the public reproduction of GPT-3 fail?
Reproduction of GPT-3● PaLM (Google) - "success", but non-public● OPT (Meta) - "fail"● BLOOM (BigScience Community led by Hugging Face) - faile""
Two Reasons
1. Pretraining Data: deduplication to avoid memorization/overfitting, datafiltering for higher quality data, and data diversity to ensure LLM’sgeneralizability. But, unfortunately, details of how PaLM and GPT-3
preprocessed the data, or the original pretraining data, have not been
revealed, which makes the public community difficult to reproduce them.
Why did all of the public reproduction of GPT-3 fail? In which tasks should we use GPT-3.5/ChatGPT?

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Why did all of the public reproduction of GPT-3 fail?
Reproduction of GPT-3● PaLM (Google) - success, but non-public● OPT (Meta)● BLOOM (BigScience Community led by Hugging Face)
Two Reasons
2. Training strategy: training frameworks, length of training duration, modelarchitecture/training setups, modifications during training procedure, for the purpose of
better stability and convergence when training very large models. Generally speaking, loss spikes
and divergences have been widely observed in the pretraining process with unclear reasons, thus
many modifications to training setups and model architectures have been proposed to alleviate
this. But some of them in OPT and BLOOM are not optimal solutions, which might lead to theirinferior performance. GPT-3 did not explicitly mention how they solved this issue.
Why did all of the public reproduction of GPT-3 fail? In which tasks should we use GPT-3.5/ChatGPT?

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The Implications of LLMs: The Landscape of NLP Researchfrom hand-crafted features to architectural engineering to pretraining to prompting
Zelle and Mooney (1996)
Hwang et al. (2019)
Dong and Lapata (2016)

Zettlemoyer and Collins (2005)

Wang et al. (2019)
Jia and Liang (2016)

Non-Neural Network

E2E Neural Networks

Contextualized Embeddings andPretrained Language Models

LLMs

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The Implications of LLMs: The Landscape of NLP Researchfrom basic NLP technologies to individual NLP applications to high-level research topics
Basic Technologies●Language Modeling●Part-of-speech Tagging●
Syntactic Parsing●Dependency Parsing●
Named Entity recognition●Coreference resolution●
Word Sense Disambiguation●Semantic Role Labelling●…...

Tasks and Applications●
Sentiment Analysis●
Information Extraction●
Machine Translation●Question Answering●
Semantic Parsing●
Summarization●
Dialogue systems●Language and Vision●
Data-to-Text Generation●…...

High-level research topics●
In-Context Learning●
Reasoning and Emergent Abilities●
Knowledge●
Multimodal Language Model●
Multilingual Language Model●
Task Generalization●
Efficiency●
Robustness and Evaluation●
Data●
Security and Privacy●
Social Impacts●…

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The Implications of LLMs: The Landscape of NLP Research
Many research efforts for individual NLP areas and tasks will start to merge into
the research of LLMs, and fade away.
When shall we stop working on an individual NLP task?
either● When LLM outperforms human performance (e.g., GLUE and SuperGLUE).
or● When LLM+prompting outperforms finetuning on specific tasks.

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Impacts on NLP Researchers
Many top-notch researchers joining/rejoining OpenAI45

So where did OpenAI researchers go?

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How should AI systems behave, and who should decide?

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How should AI systems behave, and who should decide?

https://openai.com/blog/how-should-ai-systems-behave/48

Is Large Language Model the right way to AI?

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More AI Debates
The ICML Debate: Will Progress towards Achieving AI be Mostly Driven byEngineering or Science?
AI Debate: Yoshua Bengio and Gary Marcus on the best way forward for AI
AI Debate 2: Moving AI Forward: An Interdisciplinary Approach
AI Debate 3: The AGI Debate
Is there more to NLP than Deep Learning?

50What's your experience and thought?
1.
2.
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4.
5.
6.

7.

Have you tried ChatGPT?
How did you use ChatGPT? Were you
impressed or dismissive?
Shall we continue build larger and larger
language models?
Do you think ChatGPT will replace traditional
search engines like Google?
How ChatGPT impacts technology and society?
Will AI become sentient and gain free will as
humans (but wait, what is free will, and do
humans really have free will)?
Will humans be immortal and omniscient as
programs?

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