https://notes.aimodels.fyi/self-rag-improving-the-factual-accuracy-of-large-language-models-through-self-reflection/

A cool paper. Using the LLM to judge value of new inputs.
I am always skeptical of summaries of journal articles. Even well meaning people can accidentally distort the conclusions.

Still LLM is a bullshit generator that can check bullshit level of inputs.

https://adamkarvonen.github.io/machine_learning/2024/01/03/chess-world-models.html

However, this only worked for a model trained on a synthetic dataset of games uniformly sampled from the Othello game tree. They tried the same techniques on a model trained using games played by humans and had poor results. To me, this seemed like a major caveat to the findings of the paper which may limit its real world applicability. We cannot, for example, generate code by uniformly sampling from a code tree.

Author later discusses training on you data versus general datasets.

I am out of my depth, but does not seem to provide strong evidence for the modem not just repeating information that shows up a lot for the given inputs.

https://arxiv.org/abs/2310.02207

2 author paper with interesting evidence. Again, evidence not proof. Wait for the papers that cite this one.

https://poke-llm-on.github.io/

Reinforcement learning. Cool project. Still no need to “know” anything. I usually play this type of have with short rules and monitoring the current state.

https://notes.aimodels.fyi/researchers-discover-emergent-linear-strucutres-llm-truth/

References a 2 author paper. I am not an expert in the field, but it is important to read the papers that reference this one. Those papers will have criticisms that are thought out. In general, fewer authors means less debate between the authors and easier to miss details.