How Many Labelled Examples Do You Need for a BERT-sized Model to Beat GPT4 on Predictive Tasks?

Name: How Many Labelled Examples Do You Need for a BERT-sized Model to Beat GPT4 on Predictive Tasks?
Uploaded: 2023-11-02T04:00:02.000Z
Duration: 56 min 25 s

Evaluating Large Language Models for Predictive Tasks

The purpose of this talk is to evaluate the performance of large language models, specifically GPT-4, on predictive tasks such as classification. The speaker discusses the comparison between using these models and traditional methods, as well as the challenges in conducting experiments with GPT-4.

Introduction

The goal is to assess how well large language models like GPT-4 perform on predictive tasks.

These tasks include classification and other similar tasks that can be done through zero-shot learning or in-context learning.

The speaker mentions that they will primarily refer to experiments conducted with GPT-3 from the literature, but also discuss some additional experiments with GPT-3 and Cloud 2.

Importance of Generative AI

The speaker shares a personal anecdote about their disrupted travel plans and uses it as an opportunity to demonstrate generative AI capabilities.

They mention generating cartoons related to their baggage being taken away at the airport gate.

This serves as an example of how generative AI can be used in daily workflows.

Addressing LLM Skepticism

The speaker acknowledges that there are skeptics who question the effectiveness of large language models (LLMs).

They argue that these skeptics may not fully understand the practical benefits of using LLMs in real-world scenarios.

Examples are provided where LLMs show impressive language understanding and problem-solving abilities beyond simple question answering.

Speaker's Background

The speaker introduces themselves and their experience working in the field.

They mention their open-source library called Spacey, which is widely used for various natural language processing tasks.

Another tool mentioned is Prodigy, an annotation tool known for its flexibility in deconstructing annotation tasks efficiently.

Introduction to Prodigy Teams

-The upcoming product, Prodigy Teams, is introduced as a cloud-based collaborative version of Prodigy.

The speaker encourages the audience to check it out and sign up for the beta.

Overview of the Talk

The speaker provides an overview of what will be covered in the talk.

Talk Overview

The speaker gives a brief summary of what will be discussed in the following sections.

They mention that they will focus on evaluating GPT-4's performance on predictive tasks and explore why it may not be as straightforward as it seems.

Introduction to Spacey and Prodigy

The speaker provides an introduction to Spacey and Prodigy, two tools developed by their company, Explosion.

Spacey

Spacey is an open-source library released in 2015 for natural language processing tasks.

It is widely used in thousands of companies for tasks such as entity recognition, tokenization, and grammatical analysis.

Spacey is known for its efficiency and developer-friendly experience.

Prodigy

Prodigy is an annotation tool developed by Explosion.

It allows users to script annotation tasks and optimize human time through flexible task deconstruction.

A Python script example is shown to demonstrate how examples can be queued up for annotation using Prodigy.

Introduction to Prodigy Teams

-The upcoming product, Prodigy Teams, is introduced as a cloud-based collaborative version of Prodigy.

Introduction and Premise

The speaker introduces the premise of the talk, which is to explore the use of generative models in complementing predictive models for better language understanding. They highlight that prompting large language models (LLMs) is not optimal for predictive tasks and current approaches perform poorly on such tasks.

Generative Models vs Predictive Models

Generative models and predictive models serve different needs and application contexts.

Generative models are useful for tasks like summarization, reasoning, problem-solving, paraphrasing, question answering, etc.

Predictive models involve producing fixed labels such as text classification or entity recognition.

Leveraging Language Understanding for Better Predictive Models

The speaker discusses how to utilize breakthroughs in language understanding to improve predictive models. They emphasize the need for both generative and predictive models.

Complementing Generative and Predictive Models

Generative models should complement predictive models rather than replacing them.

While generative models enable more ambitious tasks, there is still a need for data manipulation, working with numbers, and mapping text into data.

Programs require symbolic data rather than continuous representations provided by language models.

Subtasks in Data Extraction

Extracting information from text requires subtasks like named entity recognition, entity disambiguation, currency normalization, etc.

These subtasks cannot be efficiently performed solely by large language models.

Evaluating Large Language Models for Specific Tasks

The speaker explores whether large language models alone can effectively perform specific tasks like named entity recognition.

Early Classifiers vs Large Language Models

Early classifiers like perceptron taggers were effective in performing specific tasks using simple code and training techniques.

Large language models may not outperform early classifiers on certain problems like text classification.

Conceptual Distinction in Large Language Models

The speaker discusses the conceptual distinction when referring to large language models.

Generic Objective vs Specific Task

Large language models can have a generic objective, such as a language model objective.

Specific tasks require targeted models rather than relying solely on large language models.

Timestamps are provided for each section to help locate specific parts of the video.

New Section

In this section, the speaker discusses the difference between task-specific models and zero-shot learning in natural language processing tasks.

Task-Specific Models vs. Zero-Shot Learning

Task-specific models are trained with specific objectives and task-specific data.

These models are designed to map output vectors to the desired task.

Contrastingly, zero-shot learning involves initializing weights and training a model without any specific task-related information.

The speaker compares these two approaches in terms of their effectiveness.

New Section

This section explores the performance of GPT 3.5 and GPT 4 in named entity recognition tasks.

Performance of GPT 3.5 and GPT 4 in Named Entity Recognition

A recent paper from CMU compares the performance of GPT 3.5, GPT 4, and other models in named entity recognition tasks.

Surprisingly, both GPT 3.5 and GPT 4 perform poorly on this simple task compared to state-of-the-art models from 2003.

Even though GPT 4 has knowledge about this task in its training data, it still achieves lower accuracy than older models.

The results indicate that these language models struggle with basic tasks like named entity recognition.

New Section

This section presents an overview of various experiments evaluating different versions of GPT models on multiple NLP tasks.

Evaluation of Different Versions of GPT Models

A comprehensive study evaluates various versions of GPT models on text classification, part-of-speech tagging, entity recognition, etc.

Across all these tasks, the accuracy achieved by the evaluated models is significantly lower than the current state-of-the-art.

The study also highlights that crowd workers perform worse than GPT models on these tasks.

However, it is important to note that comparing model performance to human annotation as the gold standard may not accurately reflect the models' true capabilities.

New Section

This section discusses the limitations of relying solely on language models for task accuracy without human annotation.

Limitations of Language Models Without Human Annotation

Relying solely on language models like GPT for task accuracy without human annotation can lead to disappointing results.

The accuracy achieved by language models alone is lower compared to models trained with good annotation processes and task-specific objectives.

It is crucial to consider the importance of human involvement in achieving higher accuracy in tasks like named entity recognition and text classification.

The transcript provided does not contain enough information for further sections.

Large Language Models for Zero/Few-Shot Learning

In this section, the speaker discusses the use of large language models for zero/few-shot learning tasks and their performance compared to smaller models.

Large Language Models Outperforming Chat GPT

A 7 billion parameter model is introduced that outperforms chat GPT and other larger models in solving general tasks.

This model shows promise for zero/few-shot learning.

Training Large Language Models on Specific Tasks

The base model remains competitive even after task supervision.

Training large language models specifically for zero/few-shot learning is a step forward in utilizing them effectively.

Poor Performance of Large Language Models on Predictive Tasks

There is a general pattern across literature where large language models perform poorly on predictive tasks.

Few examples conducted on GPT3 also show limited accuracy, contrary to initial expectations.

Success Story: Sentiment Analysis Task

Large language models perform well on sentiment analysis tasks, achieving around 94% accuracy.

However, they are slower compared to smaller models.

Challenges in Zero/Few-Shot Learning with Text Classification

Classifying news articles into different categories proves challenging for zero/few-shot learning.

Having numerous classes makes it difficult for the model to differentiate between them.

Longer text examples require summarization and distillation, adding complexity to the prompt.

Limited Performance on Tasks with Many Classes

A task with 77 classes demonstrates poor performance even with few examples per prompt.

Small models outperform large ones even with only 5% of the data.

Experimenting with Examples Needed to Beat GPT4

In this section, the speaker discusses experiments conducted to determine how many examples are needed to surpass GPT4 in performance.

Challenges with GPT4 Timing

When using GPT4, timing becomes an issue during the experiments.

The transcript ends abruptly, and no further information is provided.

Understanding the Performance of Language Models

In this section, the speaker discusses the performance of different language models on predictive tasks and highlights the limitations of using large language models as classifiers.

Evaluating Language Models

The speaker wanted a task that was not something commonly seen before.

The CLAE 2 and GPT3 models performed similarly at around 18% accuracy.

GP4 showed slightly better results at about 30% accuracy.

However, even with limited data, a CNN model outperformed these language models with only 20 examples.

Challenges in Predictive Tasks

The baseline accuracy for these tasks is generally low.

Using large language models as classifiers is not an efficient use of resources.

It is expensive, has high latency, and yields worse results compared to previous methods.

The advantage of using prompts to avoid labeled data is outweighed by the effort required to fine-tune prompts.

Annotation with labeled examples is more reliable and provides consistent improvement.

Abstracting the Problem

Instead of relying solely on large language models, consider different computational devices for task allocation.

Treat humans as computational devices that can be assigned tasks.

Language models (LLMs) are another type of device that can be utilized for problem-solving.

CPU and GPU scheduling can serve as an analogy for allocating work between humans and LLMs.

Workflow Example with Prodigy Tool

Prodigy tool allows flexible program writing to work through examples and queue up data differently.

Examples are generated from the stream and used to update the model iteratively based on human feedback.

This workflow enables more effective task allocation between humans and LLMs.

Adapting Task Design

If an LLM struggles with learning over a small number of labels, change the task design:

Ask for one label at a time.

Use the LLM as an annotator in conjunction with a human to identify disagreements and have them adjudicated.

By utilizing different sources of information and devices, more effective data compilation can be achieved.

Efficient Annotation Principles

Humans have high latency and cannot switch tasks immediately.

Avoid excessive annotation by going over the same example multiple times with a simple annotation scheme.

It is better to avoid complex annotation manuals that exhaustively annotate everything on one sample.

Scheduling Computation on Humans

This section focuses on scheduling computation on humans efficiently and avoiding unnecessary repetition in annotation tasks.

Human Computation Considerations

Humans have high latency and require time to boot up and get into the task.

Avoid overwhelming working memory by not overloading humans with too many annotations at once.

Efficient Annotation Strategies

Don't "thr thee" (thoroughly) annotate every detail of a dataset repeatedly.

Instead, go over the same example multiple times with a simpler annotation scheme.

Summary:

The speaker discusses the performance of language models on predictive tasks. Large language models are found to be inefficient when used as classifiers due to their high cost, latency, and worse results compared to previous methods. The speaker suggests abstracting the problem by considering different computational devices such as humans and language models for task allocation. A workflow example using the Prodigy tool is provided, highlighting how examples can be generated from the stream and used iteratively to update models based on human feedback. Adapting task design and efficient annotation principles are also discussed, emphasizing the importance of scheduling computation on humans effectively and avoiding excessive repetition in annotation tasks.

New Section Efficient Computation and Focus

In this section, the speaker discusses the importance of arranging computation efficiently to maintain focus and make quick decisions.

Efficient Computation for Focus

The speaker emphasizes that it doesn't matter how many times you loop through data if you arrange the computation efficiently.

By arranging computation efficiently, people can stay in the zone, stay focused, and make quick decisions without feeling overwhelmed or forgetting things.

This efficient arrangement allows individuals to utilize their time more effectively.

Utilizing LM for Efficient Computation

The same principle applies to LLMS (Language Models), where different methods can be used to arrange computation efficiently.

By optimizing LM usage, one can maximize the benefits of all available devices.

The ultimate goal is to develop a program that recognizes specific patterns or elements.

Timestamps are not provided for the second part of the transcript.