Why is o1 so deceptive?

Great! I'd love to have included a remark that one, as a human, might anticipate forward-chainy/rational reasoning in these systems because we're often taking the "thought" metaphor seriously/literally in the label "chain-of-thought", rather than backwardy/rationalization "reasoning". 

But since it is is at least somewhat intelligent/predictive, it can make the move of "acausal collusion" with its own tendency to hallucinate, in generating its "chain"-of-"thought". That is, the optimization to have chain-of-thought in correspondence with its output can work in the backwards direction, cohering with bad output instead of leading to better output, a la partial agency [? · GW].

(Admittedly human thoughts do a lot of rationalization as well. So maybe the mistake is in taking directionality implied by "chain" too seriously?)

Maybe this is obvious [LW · GW], but it could become increasingly reckless to not notice when you're drawing the face of "thoughts" or "chains" on CoT shoggoth-movements . You can be misled into thinking that the shoggoth is less able to deceive than it actually is.

Less obvious but important: in the reverse direction, drawing  "hacker faces" on the shoggoth, as in the case of the Docker hack (section 4.2.1), can mislead into thinking that the shoggoth "wants" to or tends to hack/undermine/power-seek more than it actually, independently does. It seems at least somewhat relevant that the docker vulnerability was exploited for a challenge that was explicitly about exploiting vulnerability. Even though it was an impressive meta-hack, one must wonder how much this is cued by the prompt and therefore is zero evidence for an autonomy telos---which is crucial for the deceptive optimizer story---even though mechanistically possible. 

(The word "independently" above is important: if it takes human "misuse"/participation to trigger its undermining personas, we also might have more of a continuous shot at pausing/shutdown or even corrigibilty.)

I was going to post this as a comment, but there's also an answer here: I'd say calling o1 "deceptive" could be as misleading as calling it aligned if it outputs loving words. 

It has unsteady referentiality, at least from the POV of the meanings of us life-forms. Even though it has some closeness to our meanings and referentiality, the quantity of the unsteadiness of that referentiality can be qualitative. Distinguishing "deceptively aligned mesa-optimizer" from "the tentacles of the shoggoth I find it useful to  call 'words' don't work like 'words' in some annoying ways" is important, in order to protect some of that (quantitatively-)qualitative difference. Both for not dismissing risks and for not hallucinating them.

I notice that o1's behavior (it's cognitive process) looks suspiciously like human behaviors:

• Cognitive dissonance: o1 might fabricate or rationalize to maintain internal consistency of conflicting data (which means there is inconsistency).
• Impression management/Self-serving bias: o1 may attempt to appear knowledgeable or competent, leading to overconfidence because it is rewarded for the look more than for the content (which means the model is stronger than the feedback).

But why is this happening more when o1 can reason more than previous models? Shouldn't that give it more ways to catch its own deception? 

No:

1. Overconfidence in plausibility: With enhanced reasoning, o2 can generate more sophisticated explanations or justifications, even when incorrect. o2 "feels" more capable and thus might trust its own reasoning more, producing more confident errors ("feels" in the sense of expecting to be able to generate explanations that will be rewarded as good).
2. Lack of ground-truth: Advanced reasoning doesn't guarantee access to verification mechanisms. o2 is rewarded for producing convincing responses, not necessarily for ensuring accuracy. Better reasoning can increase the capability to "rationalize" rather than self-correct.
3. Complexity in mistakes: Higher reasoning allows more complex thought processes, potentially leading to mistakes that are harder to identify or self-correct. 

Most of this is analogous to how more intelligent people ("intellectuals") can generate elaborate, convincing—but incorrect—explanations that cannot be detected by less intelligent participants (who may still suspect something is off but can't prove it). 

Why do you think o1 would know that it's making up references? The fact that it can't retrieve URLs is completely different from, and unrelated to, its knowledge of URLs. LLMs do not need retrieval to know about many web pages. GPT-3 and GPT-4 know tons about tons of URLs! (In fact, I actively exploit this to save myself effort linking stuff on gwern.net - no retrieval necessary.)

Let's take the Sally link. The URL may not exist, but Sally's Baking Addiction does (unsurprisingly, as there are lots of cooking-related websites and why confabulate more than necessary) and has many brownie recipes, and some ginger probing of 4o (to avoid any issues with o1 training and just try to understand the knowledge of the baseline) suggests that 4o finds the URL real and doesn't discuss it being fake or confabulated: https://chatgpt.com/share/66f753df-2ac4-8006-a296-8e39a1ab3ee0

I'll note that malicious compliance is a very common response to being provided a task that's not straightforwardly possible with the resources available, and no channel to simply communicate that without retaliation.  BS an answer, or technically correct/rules as written response, is often just the best available strategy if one isn't in a position to fix the evaluator's broken incentives.

An actual human's chain of thought would be a lot spicier if their boss ask them to produce a document with working links without providing internet access.