Hubinger, et. al. "SLEEPER AGENTS: TRAINING DECEPTIVE LLMS THAT PERSIST THROUGH SAFETY TRAINING". Arxiv: 2401.05566v3. Jan 17, 2024.

Very disturbing and interesting results from team of researchers from Anthropic and elsewhere.

Reading this one on Nov 27, 2023 for the reading group.

Reading this one on Nov 27, 2023 for the reading group.

Introduction of the RoBERTa improved analysis and training approach to BERT NLP models.

(Chen, NeurIPS, 2021) Che1, Lu, Rajeswaran, Lee, Grover, Laskin, Abbeel, Srinivas, and Mordatch. "Decision Transformer: Reinforcement Learning via Sequence Modeling". Arxiv preprint rXiv:2106.01345v2, June, 2021.

Quickly a very influential paper with a new idea of how to learn generative models of action prediction using SARSA training from demonstration trajectories. No optimization of actions or rewards, but target reward is an input.

Wu, Prabhumoye, Yeon Min, Bisk, Salakhutdinov, Azaria, Mitchell and Li. "SPRING: GPT-4 Out-performs RL Algorithms byStudying Papers and Reasoning". Arxiv preprint arXiv:2305.15486v2, May, 2023.

Them's fighten' words!

I haven't read it yet, but we're putting it on the list for this fall's reading group. Seriously, a strong result with a very strong implied claim. they are careful to say it's from their empirical results, very worth a look. I suspect that amount of implicit knowledge in the papers, text and DAG are helping to do this.

The Big Question: is their comparison to RL baselines fair, are they being trained from scratch? What does a fair comparison of any from-scratch model (RL or supervised) mean when compared to an LLM approach (or any approach using a foundation model), when that model is not really from scratch.

Wang et. al. "Scientific discovery in the age of artificial intelligence", Nature, 2023.

A paper about the current state of using AI/ML for scientific discovery, connected with the AI4Science workshops at major conferences.

(NOTE: since Springer/Nature don't allow public pdfs to be linked without a paywall, we can't use hypothesis directly on the pdf of the paper, this link is to the website version of it which is what we'll use to guide discussion during the reading group.)

Description: Reinforcement learning uses neural networks to generate a mathematical expression sequentially by adding mathematical symbols from a predefined vocabulary and using the learned policy to decide which notation symbol to be added next. The mathematical formula is represented as a parse tree. The learned policy takes the parse tree as input to determine what leaf node to expand and what notation (from the vocabulary) to add.

Zecevic, Willig, Singh Dhami and Kersting. "Causal Parrots: Large Language Models May Talk Causality But Are Not Causal". In Transactions on Machine Learning Research, Aug, 2023.

GPT-2 Introduction paper

Language Models are Unsupervised Multitask Learners A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, and I. Sutskever, (2019).

"Attention is All You Need" Foundational paper introducing the Transformer Architecture.

GPT-3 introduction paper

"Are Pre-trained Convolutions Better than Pre-trained Transformers?"

"LaMDA: Language Models for Dialog Application" Meta's introduction of LaMDA v1 Large Language Model.

Benyamin GhojoghAli Ghodsi. "Attention Mechanism, Transformers, BERT, and GPT: Tutorial and Survey"