See also: Your posts should be on Arxiv

I do agree we're leaving lots of value on the table and even causing active harm by not writing things up well, at least for Arxiv, for a bunch of reasons including some of the ones listed here. 

It's good to see some informed critical reflection on MI as there hasn't been much AFAIK. It would be good to see reactions from people who are more optimistic about MI!

I see. In that case, what do you think of my suggestion of inverting the LM? By default, it maps human reward functions to behavior. But when you invert it, it maps behavior to reward functions (possibly this is a one-to-many mapping but this ambiguity is a problem you can solve with more diverse behavior data). Then you could use it for IRL (with the some caveats I mentioned).

Which may be necessary since this:

The LM itself is directly mapping human behaviour (as described in the prompt) to human rewards/goals (described in the output of the LM).

...seems like an unreliable mapping since any training data of the form "person did X, therefore their goal must be Y" is firstly rare and more importantly inaccurate/incomplete since it's hard to describe human goals in language. On the other hand, human behavior seems easier to describe in language.

Do I read right that the suggestion is as follows:

• Overall we want to do inverse RL (like in our paper) but we need an invertible model that maps human reward functions to human behavior.
• You use an LM as this model. It needs to take some useful representation of reward functions as input (it could do so if those reward functions are a subset of natural language)
• You observe a human's behavior and invert the LM to infer the reward function that produced the behavior (or the set of compatible reward functions)
• Then you train a new model using this reward function (or functions) to outperform humans

This sounds pretty interesting! Although I see some challenges:

• How can you represent the reward function? On the one hand, an LM (or another behaviorally cloned model) should use it as an input so it should be represented as natural language. On the other hand some algorithm should maximize it in the final step so it would ideally be a function that maps inputs to rewards.
• Can the LM generalize OOD far enough? It's trained on human language which may contain some natural language descriptions of reward functions, but probably not the 'true' reward function which is complex and hard to describe, meaning it's OOD.
• How can you practically invert an LM?
• What to do if multiple reward functions explain the same behavior? (probably out of scope for this post)

Great to see this studied systematically - it updated me in some ways.

Given that the study measures how likeable, agreeable, and informative people found each article, regardless of the topic, could it be that the study measures something different from "how effective was this article at convincing the reader to take AI risk seriously"? In fact, it seems like the contest could have been won by an article that isn't about AI risk at all. The top-rated article (Steinhardt's blog series) spends little time explaining AI risk: Mostly just (part of) the last of four posts. The main point of this series seems to be that 'More Is Different for AI', which is presumably less controversial than focusing on AI risk, but not necessarily effective at explaining AI risk.

Not sure if any of these qualify but: Military equipment, ingredients for making drugs, ingredients for explosives, refugees and travelers (being transferred between countries), stocks and certificates of ownership (used to be physical), big amounts of cash. Also I bet there was lots of registration of goods in planned economies.

One way to convert: measure how accurate the LM is at word-level prediction by measuring its likelihood of each possible word. For example the LM's likelihood of the word "[token A][token B]" could be $p\left(\text{token A}|\text{context}\right)\ast p(\text{token B}|$$\text{token A, context})$.

Playing this game made me realize that humans aren't trainged to predict at the token-level. I don't know the token-level vocabulary; and made lots of mistakes by missing spaces and punctuation. Is it possible to convert the token-level prediction in to word-level prediction? This may get you a better picture of human ability.

Relevant: Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations. 

They argue that the pre-trained network already learns some non-confused features but doesn't use them. And you just need to fine-tune the last layer to utilize them.

We’ll be able to fine-tune in the test environment so won’t experience OOD at deployment, and while changes will happen, continual fine-tuning will be good enough to stop the model from ever being truly OOD. We think this may apply in settings where we’re using the model for prediction, but it’s unclear whether continual fine-tuning will be able to help models learn and adapt to the rapid OOD shifts that could occur when the models are transferred from offline learning to online interaction at deployment.

Couldn't the model just fail at the start of fine-tuning (because it's causally confused), then learn in a decision setting to avoid causal confusion, and then no longer be causally confused? 

If no - I'm guessing you expect that the model only unlearns some of its causal confusion. And there's always enough left so that after the next distribution shift the model again performs poorly. If so, I'd be curious why you believe that the model won't unlearn all or most of its causal confusion.