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Question 1

The presentation contrasts ELIZA and modern LLMs. How does the transition from symbolic programming to artificial neural networks impact the interpretability and ethical concerns of these systems?

Answer 1

Neural networks operate as “black boxes,” making their decision-making processes difficult to interpret, unlike symbolic systems with explicit rules. This lack of transparency raises ethical concerns about accountability, bias, and fairness in AI applications.

Question 2

Explain why benchmarking in AI is considered “broken,” and propose a cognitive psychology-inspired approach to better evaluate LLMs’ abilities.

Answer 2

Benchmarks fail because LLMs often exploit dataset shortcuts without true understanding. A psychology-inspired approach would use hypothesis-driven evaluations that mimic human cognitive tasks, such as theory-of-mind tests adapted for token prediction, as suggested in the presentation.

Question 3

How do the challenges of the “last 10%” problem reflect broader limitations in AI generalization and understanding?

Answer 3

The “last 10%” highlights AI’s struggle with variability, context sensitivity, and edge cases, which require a depth of reasoning and adaptability that current systems lack. This reflects their reliance on patterns rather than conceptual understanding, as discussed in Chapter 13.

Question 4

How might LLMs’ lack of grounding in physical or social experiences affect their ability to handle theory-of-mind tasks?

Answer 4

Without grounding, LLMs cannot form causal or intentional models, leading to superficial responses in theory-of-mind tasks. Their outputs might mimic understanding but lack the depth required for accurate social reasoning.

Question 5

How do content-sensitive patterns observed in reasoning tasks challenge traditional views of symbolic reasoning, and what does this mean for AI’s potential?

Answer 5

Content-sensitive patterns suggest that LLMs can mimic human-like reasoning without explicitly following symbolic rules. This challenges traditional views by proposing that statistical models may develop novel forms of reasoning, distinct from human cognition.

Question 6

What is a key reason benchmarking is considered “broken” for evaluating LLMs, as discussed in the presentation?

a) Benchmarks fail to measure computational efficiency.
b) LLMs solve benchmarks using superficial patterns rather than deeper understanding.
c) Benchmarks only evaluate explicit bias and not implicit bias.
d) Benchmarks rely on human testers, which introduces variability.

Answer 6

b)LLMs solve benchmarks using superficial patterns rather than deeper understanding.

Question 7

Why does the presentation argue that targeted evaluation is preferable to standard benchmarking for LLMs?
a) It requires less computational power.
b) It aligns with the principle of avoiding sweeping conclusions about LLMs.
c) It allows for faster model fine-tuning.
d) It eliminates biases in the training data.

Answer 7

: b) It aligns with the principle of avoiding sweeping conclusions about LLMs.

Question 8

According to the Mitchell-Krakauer article, why is “scale is all you need” considered a controversial claim?
a) It dismisses the need for diverse training data.
b) It overlooks the importance of model interpretability.
c) It assumes that increasing model size will lead to genuine understanding.
d) It disregards the role of emergent abilities in smaller models.

Answer 8

c) It assumes that increasing model size will lead to genuine understanding.

Question 9

What principle is emphasized in the presentation to evaluate LLMs’ theory-of-mind abilities?
a) Using explicit rule-based reasoning tasks.
b) Translating cognitive tasks into token prediction tasks.
c) Measuring emotional alignment with human responses.
d) Ensuring the model has not seen the test during training.

Answer 9

b) Translating cognitive tasks into token prediction tasks.

Question 10

Which of the following is NOT a challenge identified in Chapter 13’s “last 10%” problem?
a) Speech recognition systems handling unknown words.
b) Machine translation systems interpreting idiomatic expressions.
c) Object detection systems failing on common objects.
d) AI models understanding nuanced contextual meaning.

Answer 10

c) Object detection systems failing on common objects.

Question 11

True or False: According to the presentation, modern LLMs like GPT-4 are interpretable due to their self-learning mechanisms.

Answer 11

False

Question 12

True or False: The Mitchell-Krakauer article argues that LLMs rely on statistical patterns and lack grounding in physical and social experiences.

Answer 12

True

Question 13

True or False: Chapter 13 suggests that the “last 10%” problem for speech recognition is primarily caused by computational inefficiency.

Answer 13

Answer: False

Question 14

True or False: The presentation highlights that implicit bias in LLMs can persist even in models explicitly fine-tuned to eliminate explicit bias.

Answer 14

Answer: True

Question 15

True or False: Benchmarking AI on standard datasets is still considered the best way to measure understanding and reasoning abilities.

Answer 15

Answer: False

Question 16

Three key principles of LLM psychology

Answer 16

1. Transform cognitive task into word prediction task
2. Consider (and control for) the training data
3. Avoid sweeping conclusions (and sweeping questions)

Question 17

explain what does the 1st principle mean (LLMs as next-token prediction machines)

Answer 17

At their core, Large Language Models (LLMs) are prediction machines designed to compute the likelihood of the next token (word or character) given a sequence of prior tokens.
This principle emphasizes that all behaviors exhibited by LLMs, such as reasoning or answering questions, stem from this fundamental task.
Implication: To fairly evaluate LLMs, any cognitive or reasoning task must be reframed as a next-token prediction problem.

Question 18

explain the principle 2 - Consider the training data

Answer 18

Modern LLMs are trained on astronomical amounts of data, often without full transparency regarding the datasets.
This introduces the possibility that models may have encountered test cases during training (data contamination).
Implication: Evaluations of LLMs must account for the training data to avoid overestimating their generalization abilities.

Question 19

explain the principle 3 - Avoid sweeping conclusions (and sweeping questions)

Answer 19

LLMs’ behaviors should not lead to overgeneralized claims about their capabilities or limitations.
Example: A failure in one context does not imply a lack of understanding, just as a success does not equate to humanlike reasoning.
Implication: Researchers should adopt a nuanced approach, avoiding extreme skepticism or overconfidence, and focus on specific abilities in well-defined contexts.

Question 20

Machine psychology

Answer 20

Machine psychology, is the study of artificial systems, such as large language models (LLMs), to understand their behavior and capabilities through psychological principles.
- It involves analyzing their outputs (e.g., reasoning, language use) as emergent properties of their design (e.g., next-token prediction) and training data, while emphasizing that these systems do not think or understand like humans.
- It focuses on evaluating machine “cognition” using tools and frameworks from human psychology but tailored to the limitations and mechanics of AI systems.

Question 21

can you explain nativism vs emergentism/conectionism

Answer 21

• nativism suggests that LLMs might succeed at certain tasks because their architecture mimics innate principles, like statistical learning frameworks. .
• Emergentism/Connectionism, by contrast, frames LLM abilities as arising from their exposure to vast amounts of training data and the learned patterns within, rather than any “innate” programming of specific cognitive structures.
• LMs can produce fluent text — but do they actually know the rules of grammar?
  ✅ Yes!

Question 22

If model knows grammar, then: P(grammatical) > P(ungrammatical)
whats the problem with this logic

Answer 22

• Problem: many factors affect word probability — beyond grammar!
• Solution: use minimal pars, pairs of sentences with minimal difference!
• Ideally: sentences do not occur in training data (syntactic generalisation)

Question 23

But are they truly reasoning, or are they just parroting and pattern matching?

Answer 23

Traditional (symbolic) view
► LLMs only use “simple heuristics” rather than true abstract reasoning
► Their apparent reasoning is just pattern matching from training data
Emergentist (connectionist)
► Human reasoning is not logical, but
content-sensitive and contextual
► These reasoning patterns emerge
naturally from DNN/LLM training

false dichtomy, they do both

Question 24

t or f

Both humans and LLMs much better when content supports conclusion!

Answer 24

T

Question 25

Language and Grammar:

Answer 25

LLMs demonstrate remarkable fluency and coherence in language generation, capable of producing grammatically correct sentences. They excel at capturing statistical patterns of language but lack true semantic understanding, relying on next-token prediction rather than grounded comprehension.

Question 26

REASONING

Answer 26

LLMs can perform logical and analogical reasoning tasks, but their success depends on statistical patterns in the training data rather than genuine cognitive processes. They often fail on abstract or novel reasoning tasks requiring true generalization.

Question 27

Theory of Mind:

Answer 27

While LLMs can simulate behaviors resembling a theory of mind (e.g., predicting others’ beliefs), these are surface-level approximations derived from language patterns. They lack the experiential and embodied grounding required for genuine mental state attribution.

Question 28

Bias and Prejudice:

Answer 28

LLMs inherit biases present in their training data, reflecting and amplifying societal stereotypes and prejudices. Addressing these biases requires careful dataset curation, fine-tuning, and transparency in deployment.

Question 29

eliza vs chat bot vs human mind

Answer 29

**ELIZA** Symbolic computer program Simple (~250 lines of code) Human designed Human-interpretable **Large Language Models** Artificial Neural Network Complex (billions of parameters) Self-taught (machine learning) Non-interpretable (“black box”) **The human mind** Biological neural network Complex (~100B units, 100T connections) Self-taught (evolution + learning) Non-interpretable (biological “black box”)

Question 30

what is behaviorist and cognitivist approaches to understanding LLMs and why are we switching to cognitivist

Answer 30

The behaviorist approach to understanding LLMs focuses on evaluating observable outputs, such as whether the model can mimic human-like behavior, without considering the internal cognitive processes. In contrast, the cognitivist approach seeks to understand the internal workings of the model, such as how it represents and manipulates information. The shift toward cognitivism arises from the limitations of behaviorism, as it fails to address the actual cognitive capabilities of LLMs, such as reasoning and generalization, making cognitivism more suitable for understanding and improving these models.

Question 31

why is the dichotomy of true believers vs skeptics a false dichotomy in the context of LLMs undrstanding

Answer 31

becasue understanding is not a single monolithic concept

Question 32

can LLMs expan to many abstract syntactic rules

Answer 32

yes - GPT-style transformer LLMs learn human-like, abstract syntactic knowledge, even from only 40M words (~training data of a 4 yr old)