Imagine having a rough idea for an alternative building block of large language models other the well-known transformers.
In your head, the idea appears reasonable and overcome some perceived limitations of the state of the art. Training LLM, however, seems intimidating enough as Data Sets are well-hidden treasures, and fast-enough hardware is beyond reach for the majority of individual researchers.
How can one rapidly develop and evaluate a prototype while comparing to the established methods? Are there publicly available datasets that are small enough to allow for fast iterations (i.e., training without expensive hardware) yet meaningful enough to make at least an educated guess on how a model composed of these building blocks might behave at scale? Are there standardized benchmarks for this? Where would one begin starting this journey?
Stefan Ulbrich
The upcoming generation of Large Language Models (LLMs) is expected to focus on improving efficiency, robustness, and contextual understanding. Researchers are exploring methods to make LLMs more energy-efficient, computationally lightweight, and customized for specific domains. Furthermore, ongoing research is focused on advancements in few-shot learning and multimodal capabilities, which involve integrating text and images. Stay updated on the latest developments by keeping an eye on conferences like NeurIPS, ICLR, and ACL:
- https://neurips.cc/
- AI Conference Deadlines (aideadlin.es)
- News | ACL Member Portal (aclweb.org)
- https://llmmodels.org/
Hussain Nizam , thank you much for your answer which is very much appreciated!
Yet, with my question I had a different goal in mind. Rather than on improving the state-of-the-art in LLM, I wonder how one could evaluate innovative approaches in the fundamentals. As an example, think of replacing neural transformer networks with something entirely different such as let's say a building block derived from support vector machines or something entirely novel. How could I as a researcher assess whether the approach is more capable and what would "being more capable" even mean. Can I achieve the same performance with less parameters? But now, how is "performance" defined?
Therefore, what I aimed at learning from this question is are answers to follow ups like the following: Are there metrics defined that quantify the "capabilities" of a language or sequence model. Are there benchmarks that include small scale data sets that can be worked with on consumer hardware and which results can be extrapolated to estimate a scale up to a real-life application. Are there maybe synthetic sequence data that mimic language with a controlled complexity? All that address the difficulties of doing research at the fundamental algorithmic level.
I assume this is a very challenging problem considering that the metrics I can think of for LLM are very subjective and involve the opinion consumer and might be close to impossible to put into numbers. I think on the lower levels (like a single transformer or small network) this should still be possible.
To rapidly prototype and evaluate a new LLM building block, start with smaller, publicly available datasets like WikiText-2 or IMDB. Use lightweight frameworks like PyTorch or TensorFlow and leverage resources such as Google Colab for free GPU access. Compare performance using standardized benchmarks like GLUE or SuperGLUE. For rapid iterations, consider using model distillation or pruning techniques to reduce training time.