The False Promise of Imitating Proprietary LLMs: 4 Lies

Introduction

Large language models (LLMs) are artificial intelligence (AI) trained on massive datasets of text and code. They can be used for various tasks, such as generating text, translating languages, and writing creative content.

Topictics

In recent years, there has been a growing trend of companies developing proprietary LLMs that are not publicly available. These models are often trained on much larger datasets than open-source models. They can achieve state-of-the-art performance on various tasks.

This has led some researchers and developers to explore the possibility of imitating proprietary LLMs using open-source models. This can be done by fine-tuning an open-source model on the outputs of a proprietary model.

However, a recent paper titled “The False Promise of Imitating Proprietary LLMs” argues that this approach is a false promise. The paper’s authors found that imitation models are good at mimicking the style of proprietary models but could be better at matching their factuality.

The authors also found that the performance gap between imitation models and proprietary models is substantial. This gap can only be bridged using an unwieldy amount of imitation data or more capable base LMs.

Misconceptions about Proprietary LLMs

Myth 1: Proprietary LLMs are always better than open-source LLMs

This is only sometimes true. Open-source LLMs can be just as good as proprietary LLMs, depending on the dataset they are trained on and the techniques used to fine-tune them.

Myth 2: Imitating proprietary LLMs is the best way to improve open-source LLMs

As discussed earlier, imitating proprietary LLMs is a false promise. The performance gap between imitation and proprietary models will likely remain substantial for the foreseeable future.

Myth 3: Proprietary LLMs are not used for harmful purposes

This is also not true. Proprietary LLMs can be used for harmful purposes, such as generating fake news or spreading misinformation.

Myth 4: Proprietary LLMs are not subject to bias

This is also not true. Proprietary LLMs can be biased, just like any other machine learning model. The dataset can introduce this bias they are trained on or the techniques used to fine-tune them.

It is important to be aware of these misconceptions about proprietary LLMs so that we can make informed decisions about their use.

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Risks & Avoiding Pitfalls

Risks of Imitating LLMs

• Performance Gap: As mentioned earlier, the performance gap between imitation and proprietary models is substantial. Imitation models are better at generating text, translating languages, or writing creative content than proprietary models.
• Bias: Imitation models can inherit the biases of the proprietary models they are imitating. This can lead to the generation of offensive, discriminatory, or harmful text.
• Security Vulnerabilities: Imitation models can be used to attack security systems. For example, they could generate text to evade spam filters or bypass CAPTCHAs.
• Intellectual Property Violations: Imitating proprietary models can violate intellectual property rights. This could lead to legal action being taken against the imitators.
• Data Privacy Issues: Imitating proprietary models can raise data privacy issues. This is because the imitation models may need to access the proprietary datasets that the original models were trained on.

It is important to be aware of these risks before imitating LLMs. Considering imitating an LLM, you should carefully weigh the risks and benefits.

Avoiding Pitfalls

• Use a transparent and accountable process. Make sure that the process for imitating the LLM is transparent and accountable. This means you should be able to explain how the imitation model was created and works.
• Be aware of the potential biases. Be aware of the potential biases that can be introduced into the imitation model. This includes biases in the proprietary dataset that the LLM is trained on and biases introduced by the imitation process.
• Take steps to mitigate the risks. Take steps to mitigate the risks of imitating LLMs. This could include using techniques to detect and remove bias from the imitation model or to protect the privacy of the data used to train the imitation model.

By being aware of the risks and taking steps to mitigate them, you can help ensure that LLMs are imitated responsibly and ethically.

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Case Studies on The False Promise of Imitating Proprietary LLMs

Here are some case studies on the false promise of imitating proprietary LLMs:

The Alpaca Project

The Alpaca project attempted to create an open-source model that could imitate the capabilities of ChatGPT, a proprietary LLM developed by OpenAI. The Alpaca project was unsuccessful, and the resulting model could not match the performance of ChatGPT on various tasks.

The Self-Instruct Project

The Self-Instruct project was another attempt to create an open-source model that could imitate the capabilities of a proprietary LLM. The Self-Instruct project used self-instruction, where the model was taught to follow instructions by reading text describing how to perform tasks. The Self-Instruct project was also unsuccessful, and the resulting model could not match the performance of the proprietary LLM.

The LLaMA Project

The LLaMA project is a recent attempt to create an open-source model that can imitate the capabilities of proprietary LLMs. The LLaMA project uses language modeling-assisted fine-tuning, where a weaker open-source model is fine-tuned on the outputs of a stronger proprietary model. The LLaMA project has shown some promise, but it is still too early to say whether it will be able to match the performance of proprietary LLMs.

These are just a few examples of the case studies conducted on the false promise of imitating proprietary LLMs. The results of these studies suggest that it is very difficult to imitate proprietary LLMs using open-source models. The performance gap between imitation and proprietary models will likely remain substantial for the foreseeable future.

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Key Takeaways of The False Promise of Imitating Proprietary LLMs

• Imitating proprietary LLMs is a false promise
• There are several misconceptions about proprietary LLMs
• There are several risks associated with imitating LLMs

Knowing the challenges and risks involved is important before attempting to imitate a proprietary LLM. These challenges include the problem of data, the problem of bias, and the problem of ethics.

Overall, the false promise of imitating proprietary LLMs is a complex issue with no easy answers. Knowing the challenges and risks involved is important before attempting to imitate a proprietary LLM.

• The best way to improve open-source LLMs is to develop better base models.
• It is important to use a transparent and accountable process when imitating LLMs.
• Knowing the potential biases that can be introduced into imitation models is important.
• It is important to mitigate the risks of imitating LLMs.

Citation

Gudibande, A., Wallace, E., Snell, C., Geng, X., Liu, H., Abbeel, P., Levine, S., & Song, D. (2023). The False Promise of Imitating Proprietary LLMs. ArXiv. /abs/2305.15717