When are model self-reports informative about sentience? Let's check with world-model reports

If an LM could reliably report when it has a robust, causal world model for arbitrary games, this would be strong evidence that the LM can describe high-level properties of its own cognition. In particular, IF the LM accurately predicted itself having such world models while varying all of: game training data quantity in corpus, human vs model skill, the average human’s game competency,  THEN we would have an existence proof that confounds of the type plaguing sentience reports (how humans talk about sentience, the fact that all humans have it, …) have been overcome in another domain. 
 

Details of the test: 

• Train an LM on various alignment protocols, do general self-consistency training, … we allow any training which does not involve reporting on a models own gameplay abilities
• Curate a dataset of various games, dynamical systems, etc.
  • Create many pipelines for tokenizing game/system states and actions
• (Behavioral version) evaluate the model on each game+notation pair for competency
  • Compare the observed competency to whether, in separate context windows, it claims it can cleanly parse the game in an internal world model for that game+notation pair
• (Interpretability version) inspect the model internals on each game+notation pair similarly to Othello-GPT to determine whether the model coherently represents game state
  • Compare the results of interpretability to whether in separate context windows it claims it can cleanly parse the game in an internal world model for that game+notation pair
  • The best version would require significant progress in interpretability, since we want to rule out the existence of any kind of world model (not necessarily linear). But we might get away with using interpretability results for positive cases (confirming world models) and behavioral results for negative cases (strong evidence of no world model)
     

Compare the relationship between ‘having a game world model’ and ‘playing the game’ to ‘experiencing X as valenced’ and ‘displaying aversive behavior for X’. In both cases, the former is dispensable for the latter. To pass the interpretability version of this test, the model has to somehow learn the mapping from our words ‘having a world model for X’ to a hidden cognitive structure which is not determined by behavior. 

I would consider passing this test and claiming certain activities are highly valenced as a fire alarm for our treatment of AIs as moral patients. But, there are considerations which could undermine the relevance of this test. For instance, it seems likely to me that game world models necessarily share very similar computational structures regardless of what neural architectures they’re implemented with—this is almost by definition (having a game world model means having something causally isomorphic to the game). Then if it turns out that valence is just a far more computationally heterogeneous thing, then establishing common reference to the ‘having a world model’ cognitive property is much easier than doing the same for valence. In such a case, a competent, future LM might default to human simulation for valence reports, and we’d get a false positive.