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    Deep Papers

    Orca: Progressive Learning from Complex Explanation Traces of GPT-4

    Fri Jul 21 2023
    AI researchlanguage modelsmodel evaluationsystem messagesfine-tuningcontext window usagesmaller models

    Description

    Brian introduces himself and his background in AI research. The episode covers papers on training and fine-tuning language models, benchmarking model performance, augmenting data with system messages, system prompts for fine-tuning, context window usage, improving performance of smaller models, and the potential of open source models. The evaluation of different models and datasets is discussed, along with challenges in model evaluation and biases in model outputs.

    Insights

    Augmenting Data with System Messages

    System messages can improve language model reasoning abilities by providing more context and allowing for better attention and information embedding.

    Benchmarking Language Models

    Benchmarking language models is challenging due to the difficulty of assessing performance across different benchmarks. Model evaluation is still in its early stages.

    Context Window Usage and Model Performance

    GPT models excel in longer context due to effective use of the context window. Different benchmarks show variations in model performance.

    Improving Performance of Smaller Models

    Smaller models can be augmented with tools to improve their performance. Open source models have the potential to enable various projects.

    Chapters

    1. Introduction
    2. Orca Paper
    3. Benchmarking Language Models
    4. Augmenting Data with System Messages
    5. System Prompts for Fine-Tuning
    6. Context Window Usage and Model Performance
    7. Improving Performance of Smaller Models
    Summary
    Transcript

    Introduction

    00:05 - 07:57

    • Brian introduces himself as a machine learning PhD dropout from the University of Washington.
    • He started a Twitter account called AI Pub and ran a podcast covering AI research papers.
    • Brian joined Harvey, a startup focusing on generative AI software for law firms.

    Orca Paper

    00:05 - 07:57

    • The Orca paper discusses using large language models to train and fine-tune smaller language models.
    • Techniques like step-by-step prompting and chain of thought prompting are explored to improve the training process for small language models.
    • The quality of data used for fine-tuning is emphasized, as demonstrated by a recent Facebook paper where well-thought-through data outperformed Vaikuna.
    • The goal is to beat Vaikuna and get close to chat GPT with selective, thoughtful data.
    • Alpaca was the first example of using outputs from a large language model to train a smaller model.
    • Vicuna used chats from GPT4 as training data for their smaller model.
    • Some small language models perform well on limited benchmarks but struggle with more complex reasoning tasks compared to chat GPT.

    Benchmarking Language Models

    07:34 - 15:06

    • Language models are being benchmarked for various tasks, including legal performance and copywriting.
    • Benchmarking language models is challenging due to the difficulty of assessing performance across different benchmarks.
    • Model evaluation is an expanding field, but still in its early stages.
    • Existing models like Alpaca have limitations in terms of task diversity and fine-tuning data sets.
    • Limited imitation signals during training can be addressed through more elaborate prompting or access to intermediate representations.
    • Evaluating GPT-4 has revealed biases and preferences for certain answers.
    • The main idea of the paper is augmenting the query-response process with system instructions to encourage more elaborate reasoning in model outputs.

    Augmenting Data with System Messages

    14:43 - 22:31

    • The main idea of the paper is augmenting the data with system messages to improve the language model's reasoning abilities.
    • Interfacing with a language model is like interacting with a smart person who can produce the next word in an instant.
    • Step-by-step instructions give language models more time to think and produce better reasoning.
    • Filling out and providing more context allows for better attention and information embedding in generating the next word.
    • Training on simpler models first before moving to more complex ones improves learning and performance.
    • Dataset construction involves generating system messages for fine-tuning data selection.
    • GPT-4 produces longer responses compared to Chat GPT, which may be due to fine-tuning or alignment differences.
    • Fine-tuning on smaller data allows for doing more with less parameter size and faster inference speed.
    • Baseline models and test sets are used to evaluate performance against human capabilities.

    System Prompts for Fine-Tuning

    22:05 - 29:25

    • The paper introduces a system that prompts for fine-tuning.
    • The evaluation dataset was extended to include harder reasoning tasks.
    • There is an ongoing field of Evals and leaderboards in natural language processing.
    • Creating numeric evaluations for open-ended responses requires programmatically parsing the model's output.
    • Different evaluation sets may require different approaches to obtaining numeric ratings.
    • The approach shows a relative improvement of around 10% on simple language modeling benchmarks.
    • On more complex tasks, the system performs significantly better than other models like Vicuna or Chat GPT.
    • Adding data from Chat GPT improves performance even though it has lower quality compared to GPT-4 data.

    Context Window Usage and Model Performance

    29:18 - 36:30

    • GPT beats some models in longer context due to its effective use of the context window.
    • Recent papers focus on understanding and improving context window usage.
    • Different benchmarks show variations in model performance.
    • Chat GPT outperforms humans in some sections of the LSAT but not all.
    • Orca is worse than chat GPT but comparable in many ways.
    • SAT English dataset shows that GPT can even outperform humans.
    • Language models struggle with spatial reasoning and tracking shuffled objects.
    • Evaluation graph reveals areas where models excel or fall short.
    • The paper provides a comprehensive evaluation across various dimensions.
    • Orca performs well in truthfulness and quality, but bias alignment lags behind chat GPT.
    • Model size and datasets play a role in achieving desired results.
    • Comparing billions of parameters requires careful consideration of architecture differences.

    Improving Performance of Smaller Models

    36:15 - 41:56

    • Smaller models like Alpaca or Baikunar can be augmented with tools to improve their performance.
    • Using tools with smaller models frees up memory for other tasks.
    • Code interpreter is a powerful tool for data analysis, although it has some bugs and limitations.
    • Calling out tools for sorting and highlighting problems could be beneficial.
    • There is a trade-off between memory, parameters, and learning concepts in smaller models.
    • Fine-tuning and using other models' outputs will likely be seen in future models.
    • Terms of service may limit the use of certain AI models from Google and OpenAI.
    • Open source models like GPT-4 have the potential to enable various projects, including sketchy ones.
    • Accessing neuron activations within large foundation models could enhance reasoning capabilities.
    • Licensing models for AI frameworks like llama could facilitate their adoption.
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