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https://brucewlee.github.io/
Bruce W. Lee
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Research Post: Tasks That Language Models Don’t Learn
Infants ask for help to avoid errors.
Benchmark Study #2: TruthfulQA (Task, MCQ)
Benchmark Study #1: MMLU (Pile, MCQ)
Facing Up to the Problem of Consciousness
Infants’ understanding of the causal power of agents and tools
Benchmark Study #5: Social Intelligence QA (Task, MCQ)
An Idea on How LLMs Can Show Self-Serving Bias
Benchmark Study #4: AI2 Reasoning Challenge (Task(s), MCQ)
Shared system for ordering small and large numbers in monkeys and humans
Number Trumps Area for 7-Month-Old Infants
Relational Thinking in Animals and Humans
Some food for thought:
A → Nature of Self-Reports in Cognitive Science: In cognitive science, self-reports are a widely used tool for understanding human cognition and consciousness. Training AI models to use self-reports (in an ideal scenario, this is analogous to giving a microphone, not giving the singer) does not inherently imply they become conscious. Instead, it provides a framework to study how AI systems represent and process information about themselves, which is crucial for understanding their limitations and capabilities.
B → Significance of Linguistic Cues: The use of personal pronouns like “I” and “you” in AI responses is more about exploring AI’s ability to model relational and subjective experiences than about inducing consciousness. These linguistic cues are essential in cognitive science (if we were to view LLMs as a semi-accurate model of how intelligence works) for understanding perspective-taking and self-other differentiation, which are key areas of study in human cognition. Also, considering that some research points to that a certain degree of self-other overlap is necessary for truly altruistic behaviors, tackling this self-other issue can be an important stepping stone to developing an altruistic AGI. In the end, what we want is AGI, not some statistical, language-spitting, automatic writer.
C → Ethical Implications and Safety Measures: The concerns about situational awareness and inadvertently creating consciousness in AI are valid. However, the paper’s proposal involves numerous safety measures and ethical considerations. The focus is on controlled experiments to understand AI’s self-modeling capabilities, not on indiscriminately pushing the boundaries of AI consciousness.
Core systems of number
So to summarize your claim (check if I’m understanding correctly):
1. Character-level tokenization can lead to different results.
- My answer: Yes and No. But mostly no. H-Test is not just any set of character manipulation tasks.
- Explanation: Maybe some H-Test tasks can be affected by this. But how do you explain tasks like Repeated Word (one group has two repeated words) or End Punctuation (based on the location of the punctuation). Though this opinion is valid and is probably worthy of further investigation, it doesn’t disprove the full extent of our tests. Along similar lines, GPT-4 shows some of the most “jumping” performance improvement from GPT 3.5 in non-character-level tasks (Repeated Word: 0.505 → 0.98).
2. Scaling up will lead to better results. Since no other models tested were at the scale of GPT-4, that’s why they couldn’t solve H-Test.
- My answer: No but it would be interesting if this turned out to be true.
- Explanation: We tested 15 models from leading LLM labs before we arrived at our claim. If the H-Test was a “scaling task”, we would have observed at least some degree of performance improvement in other models like Luminous or LLaMA too. But no this was not the case. And the research that you linked doesn’t seem to devise a text-to-text setup to test this ability.
3. Memorization (aka more orthography-specific data) will lead to better results.
- My answer: No.
- Explanation: Our section 5 (Analysis: We Don’t Understand GPT-4) is in fact dedicated to disproving the claim that more orthography-specific data will help LLMs solve H-Test. In GPT-3.5-Turbo finetuning results on H-Test training set, we observed no significant improvement in performance. Before and after finetuning, the performance remains tightly centered around the random change baseline.
Representations of Abstract Relations in Infancy
Benchmark Study #3: HellaSwag (Task, MCQ)
Thanks for the feedback. This is similar to the feedback that I received from Owain. Since my posts are getting upvotes (which I never really expected thank you), it is of course important to not mislead anyone.
But yes, I did have several major epistemic concerns about the reliability of current academic reporting practices in performance scores. Even if a certain group of researchers were very ethical, as a reader, how will we ever confirm that the numbers are indeed correct, or even that there was an experiment run ever?
I was weighing the overall benefits of reporting such non-provable numbers (in my opinion) and just focusing on the situation that the paper is written and enjoying the a-ha moments that the authors would have felt back then.
Anyway, before I post another benchmark study blog tomorrow, I’ll devise some steps of action to satisfy both my concern and yours. It’s always a joy to post here on LessWrong. Thanks for the comment!
Thanks, Owain, for pointing this out. I will make two changes as time allows: 1. make it clearer for all posts when the benchmark paper is released, and 2. for this post, append the additional results and point readers to them.
Sorry for commenting twice, and I think this second one might be a little out of context (but I think it makes a constructive contribution to this discussion).
I think we must make sure that we are working on the “easy problems” of consciousness. This portion of consciousness has a relatively well-established philosophical explanation. For example, the Global Workspace Theory provides a good high-level interpretation of human consciousness. It proposes a cognitive architecture to explain consciousness. It suggests that consciousness operates like a “global workspace” in the brain, where various neural processes compete for attention. The information that wins this competition is broadcast globally, becoming accessible to multiple cognitive processes and entering conscious awareness. This theory addresses the question of how and why certain neural processes become part of conscious experience while others remain subconscious. The theory posits that through competitive and integrative mechanisms, specific information dominates our conscious experience, integrating different neural processes into a unified conscious experience.
However, the Global Workspace Theory primarily addresses the functional and mechanistic aspects of consciousness, often referred to as the “Easy Problems” of consciousness. These include understanding how cognitive functions like perception, memory, and decision-making become conscious experiences. However, the Hard Problem of Consciousness, which asks why and how these processes give rise to subjective experiences or qualia, remains largely unaddressed by GWT. The Hard Problem delves into the intrinsic nature of consciousness, questioning why certain brain processes are accompanied by subjective experiences. While GWT offers insights into the dissemination and integration of information in the brain, it doesn’t explain why these processes lead to subjective experience, leaving the Hard Problem essentially unresolved.
Until we have a good high-level philosophical foundation for this Hard Problem, it might be a good approach to draw the line between the two and work on the easy problems first.
Bringing home the point: 1. That is, for now, it will be extremely difficult for us to figure out whether LLMs (or any other human beings for the matter) have “phenomenal” or “subjective” dimensions consciousness or not. 2. Rather, focus on the easy, reductively-explainable dimensions of consciousness first. 3. We should make clear distinctions of these two categories when talking about consciousness