Paper Summary
Title: The False Promise of Imitating Proprietary LLMs
Source: arXiv
Authors: Arnav Gudibande et al.
Published Date: 2023-05-25
Podcast Transcript
Hello, and welcome to paper-to-podcast. Today, we're diving into a fascinating paper that I've only read 47 percent of, but trust me, it's worth discussing. The paper is titled "The False Promise of Imitating Proprietary LLMs" by Arnav Gudibande and colleagues, published on May 25, 2023.
So, you might be wondering, what's the deal with imitating proprietary language models? Well, it turns out that it's not as effective as initially believed. I mean, imitation is the sincerest form of flattery, but when it comes to language models, it seems flattery doesn't get you very far.
The researchers found that while imitation models do improve over their base models, they still can't quite match the capabilities of the proprietary models they're trying so hard to mimic. In a twist, crowdworkers initially rated the output quality of imitation models highly, with around 70% of their outputs rated as equal or better than the proprietary model, ChatGPT. But upon conducting more targeted automatic evaluations, it was discovered that imitation models only improved on tasks that were heavily supported in the imitation data, and in some cases, even declined in accuracy. Bummer, right?
The researchers concluded that closing the capabilities gap between open-source and proprietary models would require a large and diverse imitation dataset, which may be unwieldy and impractical. Instead, they found that improving the base language models, such as by increasing their size or improving their pre-training data quality, was a more promising approach than relying on imitation.
The research methods used were pretty thorough, with the researchers creating two types of imitation datasets, one for task-specific imitation and another for broad-coverage imitation. They then fine-tuned language models of varying sizes on these datasets using different amounts of imitation data. The evaluation involved both human and automatic evaluations, as well as a qualitative analysis of the imitation models and their performance across different tasks.
The strengths of this research lie in its critical analysis of the model imitation approach and the thorough experimentation with different data sources, base model sizes, and imitation data amounts. The researchers conducted rigorous automatic and human evaluations to assess the efficacy of imitation models.
However, some limitations were observed. For instance, the imitation models might not fully capture the capabilities of proprietary language models due to the relatively small amount of imitation data used compared to the pre-training data. Additionally, the study highlights that broad-coverage imitation is more difficult to achieve than task-specific imitation, requiring a larger and more diverse dataset. Another limitation is that the automatic evaluations might not fully capture human-perceived quality, potentially leading to discrepancies between evaluations and real-world usefulness.
Despite these limitations, the research on imitation models has potential applications in various fields, such as academia and businesses. Powerful imitation models can drive new research projects, enhance understanding of complex topics, and improve existing services. However, it's essential to consider the ethical implications and potential risks of using imitation models, particularly in cases where they might be used for malicious purposes or to infringe on proprietary systems.
So, there you have it, folks! Imitating fancy chatbots might not be the golden ticket we were hoping for, but it's still an intriguing area of research that can help us better understand the world of language models. You can find this paper and more on the paper2podcast.com website.
Supporting Analysis
The paper discovered that imitating proprietary language models using open-source models is not as effective as initially believed. While imitation models do improve over their base models, they still fall short of matching the capabilities of the proprietary models they are trying to mimic. One interesting finding was that crowdworkers initially rated the output quality of the imitation models highly, with around 70% of their outputs rated as equal or better than those of the proprietary model, ChatGPT. However, upon conducting more targeted automatic evaluations, the researchers found that imitation models only improved on tasks that were heavily supported in the imitation data, and in some cases, even declined in accuracy. The researchers concluded that closing the capabilities gap between open-source and proprietary models would require a large and diverse imitation dataset, which may be unwieldy and impractical. They found that improving the base language models, such as by increasing their size or improving their pre-training data quality, was a more promising approach than relying on imitation.
The researchers explored the effectiveness of model imitation, a technique used to improve open-source language models by fine-tuning them on outputs from stronger proprietary models like ChatGPT. They created two types of imitation datasets: one for task-specific imitation (focusing on Natural Questions) and another for broad-coverage imitation (covering various behaviors, domains, and tasks). They then fine-tuned language models of varying sizes (1.5B-13B) on these datasets using different amounts of imitation data (0.3M-150M tokens). For evaluation, they conducted both human and automatic evaluations, focusing on the ShareGPT-Mix models. Human evaluation involved blind pairwise output comparisons using Mechanical Turk, while automatic evaluations measured performance on 5-shot MMLU, 3-shot Natural Questions, and 0-shot HumanEval. They also analyzed the qualitative aspects of the imitation models and their performance across different tasks. The researchers sought to understand the relationship between the amount of imitation data and model performance, as well as the effectiveness of local imitation models compared to broad-coverage models.
The most compelling aspects of the research include its critical analysis of the model imitation approach and the thorough experimentation with different data sources, base model sizes, and imitation data amounts. The researchers conducted rigorous automatic and human evaluations to assess the efficacy of imitation models, making their investigation more comprehensive. The researchers followed best practices by building imitation datasets for both task-specific and broad-coverage imitation, ensuring a wide range of scenarios were covered. They also performed qualitative analysis and crowdworker evaluation to better understand the quality of imitation models. Additionally, they conducted targeted automatic evaluations to expose the failure modes of the models. Moreover, the research highlights the trade-offs between different evaluation datasets and explores the distribution shift and tension between conversational-style fine-tuning data and downstream benchmarks. This analysis opens up new avenues for future work in understanding and mitigating performance regressions. Overall, the paper's thorough investigation, experimentation, and evaluation methods underscore the robustness and rigor of their analysis.
One possible limitation of the research is that the imitation models might not be able to fully capture the capabilities of proprietary language models, as the amount of imitation data used is relatively small compared to the pre-training data. This makes it challenging to bridge the gap in performance between open-source and closed-source language models with current methods. The study also highlights that broad-coverage imitation is more difficult to achieve than task-specific imitation, which might require a larger and more diverse dataset for successful imitation. Additionally, the research observes a trade-off between different evaluation datasets, where training on more conversational-style fine-tuning data may hurt performance on downstream benchmarks. This indicates that there might be a distribution shift, and it remains an open problem on how to mitigate the performance regressions. Furthermore, the study's automatic evaluations might not fully capture human-perceived quality, as they focus on specific tasks rather than the overall language generation capability. This could lead to potential discrepancies between the evaluations and the actual usefulness of the imitation models in real-world applications.
The research on imitation models has potential applications in various fields, including academics and businesses. In academia, powerful imitation models can drive new research projects and enhance understanding of complex topics. Companies can use imitation models to launch services that compete with proprietary systems or to improve their existing services. Furthermore, model imitation can be used to reduce the need for high-quality fine-tuning data if a sufficiently strong base language model is available. However, it is essential to consider the ethical implications and potential risks of using imitation models, particularly in cases where they might be used for malicious purposes or to infringe on proprietary systems.