The False Promise of Imitating Proprietary LLMs with GANs

The False Promise of Imitating Proprietary LLMs

In recent years, language models have seen significant advancements, with Generative Pre-trained Transformer models (GPTs) leading the way. As these models evolve, the quest to bridge the capability gap between proprietary and open-source Language Models (LLMs) becomes more pronounced. The appeal of emulating proprietary LLMs, particularly using Generative Adversarial Networks (GANs) and imitation learning, has piqued the interest of both researchers and developers. However, as we explore the false promise of imitating proprietary LLMs, it becomes clear that this approach has limitations and raises concerns regarding factuality, style, and benchmarking accuracy.

What are Proprietary LLMs?

Proprietary LLMs refer to language models developed and owned by private companies or organizations. Because of their closed-source nature, these models are often not accessible to the public. Prominent examples of proprietary LLMs include models developed by large tech giants for specific commercial applications. These proprietary LLMs have been trained on massive amounts of data and have access to proprietary fine-tuning techniques, allowing them to achieve impressive performance levels across various tasks.

What is Imitation Learning?

Imitation Learning is a subfield of machine learning where an agent learns to imitate the behaviour of an expert. In the context of language models, imitation learning involves training an open-source model to mimic the behaviour and capabilities of a proprietary LLM. The hope is that by imitating the proprietary model, the open-source model can close the capabilities gap and exhibit similar performance on various tasks.

Why Imitating Proprietary LLMs is an Attractive Proposition

The appeal of imitating proprietary LLMs stems from their outstanding performance on various natural language processing tasks. Developers and researchers believe that by replicating the behaviour of these successful models, they can create open-source alternatives that can match or even surpass their performance.

Furthermore, imitating proprietary LLMs offers a shortcut to achieving state-of-the-art results without extensive data and expensive fine-tuning procedures. It presents the possibility of democratising access to powerful language models, levelling the playing field for developers and researchers worldwide.

The Limitations of Imitation Learning

Despite its appeal, imitation learning has limitations, and imitating proprietary LLMs with GANs is challenging. Let’s explore some of these limitations:

The Capabilities Gap between Open and Closed LLMs:

Proprietary LLMs have access to vast amounts of proprietary data, allowing them to learn specific patterns and nuances that open-source models lack. Imitating these nuances through GANs and imitation learning may only partially bridge the capabilities gap. Open-source models are often constrained by the data available during their pre-training phase, leading to disparities in performance.

The Importance of Factuality:

Proprietary LLMs are often trained on diverse datasets, enabling them to generate accurate responses. However, imitation learning does not guarantee the same level of factuality in open-source models. In pursuing matching the output of the proprietary model, the open-source model might generate plausible-sounding but incorrect or misleading information.

The Limitations of Benchmarks:

Evaluating the success of imitation learning is heavily reliant on benchmarks. Proprietary models perform better than open-source models because of exclusive training data and fine-tuning techniques. Benchmarks alone may not portray the open-source model’s true potential in mimicking proprietary LLMs.

The False Promise of Imitation Learning with GANs

A study conducted by Gudibande et al. delved into the effectiveness of GANs for imitation learning to close the capabilities gap between open and closed LLMs. The research highlighted the challenges in replicating the proprietary models’ performance, even with the GAN-based imitation approach.

The study’s results showed that while GAN-based imitation learning could improve the performance of open-source models, it did not reach the same level as the proprietary LLMs. The capabilities gap persisted, showing that more than imitation learning alone might be required to achieve parity with proprietary models.

The Implications for Open-Source Language Models

The pursuit of imitating proprietary LLMs with GANs raises critical implications for the open-source language model community. It underscores the importance of continued research and development to enhance open-source models’ capabilities without relying solely on imitation learning.

Developers and researchers must focus on exploring new techniques and methods that can address the limitations of imitation learning. This includes novel approaches to fact-checking and factuality enforcement, data augmentation, and more effective benchmarking strategies, encompassing a broader range of tasks and domains.


It’s crucial to recognize the intrinsic constraints of replicating proprietary LLMs through GANs and imitation learning, even though it may be tempting. Ethical considerations for trustworthy information arise with factuality concerns and the capability gap in open-and-closed LLMs.

The path to empowering open-source language models lies in continued research, collaboration, and innovation. By addressing the limitations head-on and exploring alternative strategies, we can advance the field of natural language processing and create open-source LLMs that are more reliable, factual, and capable across diverse tasks and applications.

The Future of Open-Source Language Models

Looking ahead, the future of open-source language models holds great promise. As research progresses, we expect significant advancements in data efficiency, fine-tuning techniques, and context-aware language understanding. Open-source language models can rival proprietary ones when researchers, developers, and organizations collaborate. Promotes NLP inclusivity and fairness.

In conclusion, while the false promise of imitating proprietary LLMs with GANs raises essential challenges, it catalyzes further innovation. By acknowledging the limitations and working collaboratively to overcome them, we can usher in a new era of open-source language models that truly democratise access to innovative natural language processing capabilities.

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