Large Language Models (LLMs) like GPT-3 are static snapshots of the data they were trained on, representing a specific moment in time. Their knowledge is frozen, unable to adapt to new information or evolving worldviews. While useful for certain tasks, this inherent limitation makes them unsuitable for applications requiring up-to-date information or nuanced understanding of changing contexts. Essentially, they are sophisticated historical artifacts, not dynamic learning systems. The author argues that focusing on smaller, more adaptable models that can continuously learn and integrate new knowledge is a more promising direction for the future of AI.
Salvatore Sanfilippo, the creator of Redis, argues in his blog post "Big LLMs weights are a piece of history" that the current practice of distributing large language models (LLMs) by sharing their weights will soon become obsolete. He posits that the sheer size and computational demands of these models are reaching a point of diminishing returns. Training these massive models requires immense resources, accessible only to a handful of large corporations, and inferencing with them necessitates significant hardware capabilities, limiting widespread accessibility and deployment.
Sanfilippo believes the future of LLMs lies in distilling the knowledge embedded within these colossal models into smaller, more specialized models. He envisions a shift towards training smaller models on the outputs of the larger LLMs, effectively transferring the learned knowledge without needing to distribute the massive weight files. This approach, analogous to learning from a teacher rather than studying the entirety of a library, would allow for wider dissemination and utilization of LLM capabilities. Smaller, specialized models could be deployed on less powerful hardware, making them accessible to a broader range of users and applications.
Furthermore, Sanfilippo contends that distributing the output of large LLMs, rather than the weights themselves, provides a greater degree of control and safety. By curating the output data, developers can mitigate potential biases and inaccuracies present in the larger models, resulting in more reliable and trustworthy downstream applications. This curated data then acts as a refined training set for the smaller, specialized models.
Sanfilippo acknowledges that the output of large LLMs may not perfectly encapsulate all the nuances and intricacies of the original model. However, he argues that this trade-off is acceptable given the significant gains in accessibility, efficiency, and control afforded by utilizing smaller, distilled models. This approach, he suggests, democratizes access to advanced language processing capabilities, empowering a wider community of developers and users to leverage the power of LLMs without the constraints of massive computational resources. He concludes by expressing his excitement for this potential shift in the LLM landscape, anticipating a future where the focus moves from sheer model size to efficient knowledge transfer and specialized applications.
Summary of Comments ( 12 )
https://news.ycombinator.com/item?id=43378401
HN users discuss Antirez's blog post about archiving large language model weights as historical artifacts. Several agree with the premise, viewing LLMs as significant milestones in computing history. Some debate the practicality and cost of storing such large datasets, suggesting more efficient methods like storing training data or model architectures instead of the full weights. Others highlight the potential research value in studying these snapshots of AI development, enabling future analysis of biases, training methodologies, and the evolution of AI capabilities. A few express skepticism, questioning the historical significance of LLMs compared to other technological advancements. Some also discuss the ethical implications of preserving models trained on potentially biased or copyrighted data.
The Hacker News post titled "Big LLMs weights are a piece of history" (linking to an Antirez blog post about the potential for using LLMs as a historical record) sparked a lively discussion with several interesting comments.
Many commenters agreed with Antirez's core premise, acknowledging the inherent historical value embedded within LLM weights. They pointed out how these weights capture a snapshot of the data they were trained on, reflecting societal biases, cultural trends, and the state of knowledge at a specific point in time. This "fossilized" information, they argued, could be valuable for future researchers studying the evolution of language, culture, and technology. One commenter even suggested that future historians might "mine" these weights like archaeologists excavate ancient ruins.
Several commenters expanded on the idea, discussing the potential to analyze changes in LLM weights over time to track the evolution of language and cultural shifts. They envisioned comparing different versions of a model to identify how its understanding of certain concepts changed, potentially revealing how societal attitudes evolved.
Some commenters raised practical considerations, like the sheer size of these models and the challenges of storing and accessing them for historical analysis. They discussed the need for efficient methods to query and interpret the information encoded within the weights.
However, not everyone agreed with the central premise. Some argued that the information contained within LLM weights is too abstract and entangled to be meaningfully interpreted as a historical record. They pointed out that the weights represent complex statistical relationships rather than explicit factual information, making it difficult to extract specific historical insights. They also questioned the reliability of these models as historical sources, given their potential biases and limitations. One commenter specifically argued that LLMs are more akin to a "compressed representation" of the training data rather than a direct historical record, potentially leading to distortions and inaccuracies.
A few commenters also touched upon the ethical implications of preserving and analyzing LLM weights, particularly regarding privacy concerns. They raised questions about the potential to reconstruct sensitive information from the training data, highlighting the need for careful consideration of data privacy and security.
The discussion also branched into related topics, such as the possibility of using LLMs to generate synthetic historical data and the potential for future AI systems to actively curate and preserve their own historical records.