Stanford's Center for Research on Foundation Models posts on its AI work, including Alpaca, HELM benchmarks, and transparency indexes.
HN commenters cite it for the Alpaca instruction-tuning result, HELM model benchmarks, the music transformer, and the definition of foundation models.
Stanford CRFM | Bay Area | Full-Time | On-Site, Hybrid
Foundation models like ChatGPT, PaLM, and Stable Diffusion are transforming the world around us. The Stanford Center for Research on Foundation Models (CRFM; https://crfm.stanford.edu/), which is part of Stanford HAI, is an interdisciplinary initiative that aims to make foundation models more reliable, transparent and accessible to the world. We take on ambitious projects that seek to rigorously evaluate existing foundation models and to build new ones.
We are currently seeking a research engineer to join our engineering team. This is an unique opportunity to work with seasoned engineers who have spent many years in industry as well as PhD students, post-docs, and faculty at CRFM. You will contribute to cutting-edge research, publish papers, gain access to the latest foundation models, and be immersed in the vibrant CRFM community.
You will work on our open source software projects, including:
- HELM, a framework for holistic evaluation of large language models
- Levanter, a framework for transparent and accessible large-scale language model training
For more information or to apply, please go to https://careersearch.stanford.edu/jobs/research-engineer-21381
It's not a bold assumption. t's the only assumption. We can't control the output of llms completely because we don't know how they generate. Nobody on earth has the faintest clue how all those 175 billion paramters are shaping the response to input.
It doesn't matter bout "thinking" or whatever. Any black box system will be uncontrollable in essence. You can not make inviolable rules for a system you don't understand.
And saying LLMs hallucinate because they don't understand anything is stupid. And just shows ignorance on your part. Models hallucinate because they're rewarded for plausibly guessing during training when knowledge fails. Plausibly guessing is a much better strategy to reducing loss.
And the conclusion is obvious enough. Bugger smarter models hallucinate less because they guess less. That holds true.
https://crfm.stanford.edu/helm/latest/?group=core_scenarios
All the instruct tuned models on this list follow that trend.
From Ada to Babbage to Curie to Claude to Davinci-002/003. Greater size equals Greater truthfulness (evaluated on TruthfulQA)
I think there's a new approach for “How do you get the data?” that wasn't available when this article was written in 2015. The new text and image generative models can now be used to synthesize training datasets.
I was working on an typing autocorrect project and needed a corpus of "text messages". Most of the traditional NLP corpuses like those available through NLTK [0] aren't suitable. But it was easy to script ChatGPT to generate thousands of believable text messages by throwing random topics at it.
Similarly, you can synthesize a training dataset by giving GPT the outputs/labels and asking it to generate a variety of inputs. For sentiment analysis... "Give me 1000 negative movie reviews" and "Now give me 1000 positive movie reviews".
The Alpaca folks used GPT-3 to generate high-quality instruction-following datasets [1] based on a small set of human samples.
Etc.
[0] https://www.nltk.org/nltk_data/
[1] https://crfm.stanford.edu/2023/03/13/alpaca.html
It's worth pointing out that size isn't everything. From Meta's benchmarking [1] LLaMA 33B outperforms GPT-3 175B, Gopher 280B, Chinchilla 70B and even matches PaLM 540B on a bunch of common evals. Those interested in doing more comparisons can look at https://crfm.stanford.edu/helm/latest/?group=core_scenarios and https://paperswithcode.com/paper/llama-open-and-efficient-foundation-language-1 to see where it sits (with some GPT 3.5 and 4 numbers here: https://paperswithcode.com/paper/gpt-4-technical-report-1)
I'd agree the secret sauce for how great the newest services perform is probably in the fine-tuning. We're seeing almost daily releases of fine-tuning data sets, training methods and models (at lower and lower costs) so I'm personally pretty optimistic that we'll be seeing some big improvement in self-hosted LLM performance pretty quickly.
[1] https://ar5iv.labs.arxiv.org/html/2302.13971#:~:text=Table%203%3A,Sense%20Reasoning%20tasks.
They mostly aren't. The lack of transparency around how many parameters frontier models have and how long they're trained is a big obstacle when it comes to estimating the energy impact of training very large models.
A group at Stanford has been benchmarking model providers by transparency here: https://crfm.stanford.edu/fmti/May-2024/index.html
I think a great way to create positive change in the world is to pressure OpenAI, Anthropic, Google, XAI, and Meta to all share details about the energy cost of training and inference for their models. If every major provider provided this transparency, it would be less valuable to keep that info secret from a "keep your competitors in the dark" perspective. It would also allow customers to make decisions based on more than just performance and cost.
From the Alpaca report (https://crfm.stanford.edu/2023/03/13/alpaca.html):
> we watermark all the model outputs using the method described in Kirchenbauer et al. 2023, so that others can detect (with some probability) whether an output comes from Alpaca 7B
https://arxiv.org/abs/2301.10226
> We propose a watermarking framework for proprietary language models. The watermark can be embedded with negligible impact on text quality, and can be detected using an efficient open-source algorithm without access to the language model API or parameters. The watermark works by selecting a randomized set of "green" tokens before a word is generated, and then softly promoting use of green tokens during sampling. We propose a statistical test for detecting the watermark with interpretable p-values