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12. Apr. 2023 · Modified 1 year, 1 month ago. Viewed 295 times. 1. There is some fallacious reasoning along the lines of "Bigger is always better". Example 1: Eating food makes you satisfied. Person A has eaten more food than Person B, therefore Person A will be "more satisfied" than Person B.
1. Apr. 2024 · Bigger is not Always Better: Scaling Properties of Latent Diffusion Models. Kangfu Mei, Zhengzhong Tu, Mauricio Delbracio, Hossein Talebi, Vishal M. Patel, Peyman Milanfar. We study the scaling properties of latent diffusion models (LDMs) with an emphasis on their sampling efficiency.
4. Bigger is not always better sounds more natural and idiomatic. The comparison can be deduced from the context. – J.R. ♦. Aug 10, 2017 at 17:26. Shouldn't it be big is not always better... – user55625. Aug 10, 2017 at 17:32. 2. Bigger isn't always better is the most natural way to express this.
- Big, Bigger, Better?
- Scaling Laws
- Reasonable Concerns
- The Problems of Scale
- Smarter and smaller?
- Energy-Efficient LLMs
LLMs such as ChatGPT and Minerva are giant networks of computing units (also called artificial neurons), arranged in layers. An LLM’s size is measured in how many parameters it has — the adjustable values that describe the strength of the connections between neurons. Training such a network involves asking it to predict masked portions of a known s...
That the biggest Minerva model did best was in line with studies that have revealed scaling laws — rules that govern how performance improves with model size. A study in 2020 showed that models did better when given one of three things: more parameters, more training data or more ‘compute’ (the number of computing operations executed during trainin...
François Chollet, an AI researcher at Google in Mountain View, is among the sceptics who argue that no matter how big LLMs become, they will never get near to having the ability to reason (or mimic reasoning) well enough to solve new problems reliably. An LLM only appears to reason by using templates that it has encountered before, whether in the t...
While the debate plays out, there are already pressing concerns over the trend towards larger language models. One is that the data sets, computing power and expense involved in training big LLMs restricts their development — and therefore their research direction — to companies with immense computing resources. OpenAI has not confirmed the costs o...
For many scientists, then, there’s a pressing need to reduce LLM’s energy consumption — to make neural networks smaller and more efficient, as well as, perhaps, smarter. Besides the energy costs of training LLMs (which, although substantial, are a one-off), the energy needed for inference — in which LLMs answer queries — can shoot up as the number ...
Meanwhile, researchers are experimenting with different ways to make existing LLMs more energy efficient, and smarter. In December 2021, DeepMind reported a system called RETRO, which combines an LLM with an external database. The LLM uses relevant text retrieved from this database during inference to help it make predictions. DeepMind’s researcher...
28. März 2011 · The short answer is yes, but bigger isn't always better. Think about it: at a certain point, you'd expect length would become... well, to put it bluntly, a big problem. After all, a woman's...
17. Okt. 2019 · Why bigger is not always better: on finite and infinite neural networks. Laurence Aitchison. Recent work has argued that neural networks can be understood theoretically by taking the number of channels to infinity, at which point the outputs become Gaussian process (GP) distributed.
18. Jan. 2021 · How can we be the “bigger” person in a dispute, without compromising our values or simply resigning to agree to disagree? In other words, can you ever both be right and the bigger person? If...
