Apple researchers introduce SeedLM, a novel approach to drastically compress large language model (LLM) weights. Instead of storing massive parameter sets, SeedLM generates them from a much smaller "seed" using a pseudo-random number generator (PRNG). This seed, along with the PRNG algorithm, effectively encodes the entire model, enabling significant storage savings. While SeedLM models trained from scratch achieve comparable performance to standard models of similar size, adapting pre-trained LLMs to this seed-based framework remains a challenge, resulting in performance degradation when compressing existing models. This research explores the potential for extreme LLM compression, offering a promising direction for more efficient deployment and accessibility of powerful language models.
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.
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.
Summary of Comments ( 17 )
https://news.ycombinator.com/item?id=43599967
HN commenters discuss Apple's SeedLM, focusing on its novelty and potential impact. Some express skepticism about the claimed compression ratios, questioning the practicality and performance trade-offs. Others highlight the intriguing possibility of evolving or optimizing these "seeds," potentially enabling faster model adaptation and personalized LLMs. Several commenters draw parallels to older techniques like PCA and word embeddings, while others speculate about the implications for model security and intellectual property. The limited training data used is also a point of discussion, with some wondering how SeedLM would perform with a larger, more diverse dataset. A few users express excitement about the potential for smaller, more efficient models running on personal devices.
The Hacker News thread for "SeedLM: Compressing LLM Weights into Seeds of Pseudo-Random Generators" contains several interesting comments discussing the feasibility, implications, and potential flaws of the proposed approach.
Several commenters express skepticism about the practical applicability of SeedLM. One points out that the claim of compressing a 7B parameter model into a 100KB seed is misleading, as training requires an enormous amount of compute, negating the storage savings. They argue this makes it less of a compression technique and more of a novel training method. Another user expands on this by questioning the efficiency of the pseudo-random generator (PRG) computation itself. If the PRG is computationally expensive, retrieving the weights could become a bottleneck, outweighing the benefits of the reduced storage size.
A related thread of discussion revolves around the nature of the PRG and the seed. Commenters debate whether the seed truly encapsulates all the information of the model or if it relies on implicit biases within the PRG's algorithm. One comment suggests the PRG itself might be encoding a significant portion of the model's "knowledge," making the seed more of a pointer than a compressed representation. This leads to speculation about the possibility of reverse-engineering the PRG to understand the learned information.
Some users delve into the potential consequences for model security and intellectual property. They suggest that if SeedLM becomes practical, it could simplify the process of stealing or copying models, as only the small seed would need to be exfiltrated. This raises concerns about protecting proprietary models and controlling their distribution.
Another commenter brings up the potential connection to biological systems, wondering if something akin to SeedLM might be happening in the human brain, where a relatively small amount of genetic information gives rise to complex neural structures.
Finally, a few comments address the experimental setup and results. One commenter questions the choice of tasks used to evaluate SeedLM, suggesting they might be too simple to adequately assess the capabilities of the compressed model. Another points out the lack of comparison with existing compression techniques, making it difficult to judge the relative effectiveness of SeedLM.
Overall, the comments reflect a mixture of intrigue and skepticism about the proposed SeedLM approach. While acknowledging the novelty of the idea, many users raise critical questions about its practical viability, computational cost, and potential security implications. The discussion highlights the need for further research to fully understand the potential and limitations of compressing large language models into pseudo-random generator seeds.