Short Story Assignment based on paper "LLM Cognitive Judgements Differ From Human"

1. LLM Cognitive
Judgements Differ
From Human

2. The Gap in Cognitive Judgement
● Cognitive Judgement: Decision-making based on incomplete information
● Highlight: Growing interest in studying LLMs as cognitive subjects
● Focus: LLMs like GPT-3 and ChatGPT's cognitive judgments on limited-data tasks.

3. Inductive Reasoning as a Research Focus
● Definition: Generalizing from specific instances
● Context: Extensive investigations of LLMs in NLP reveal limitations in reasoning,
especially inductive reasoning.
● Relevance: Inductive reasoning as a core element of cognitive judgment

4. Research Questions and Hypothesis
● Hypothesis: LLMs may not process inductive reasoning tasks as humans do
● Research Questions: How do LLMs handle inductive reasoning? Are their thought
processes different from humans?

5. Methodological Framework
● Task Design: Predicting extent/duration of phenomena from intermediate values
● Comparison: Responses from 350 participants vs. Bayesian predictor
● Specific Tasks: Cake baking times, life spans, movie grosses, poem lengths, U.S.
representatives' terms, telephone box office waiting times.

6. Key Findings on LLM Judgements
● Finding: LLMs' judgments differ significantly from human judgments
● Metric: Mean Average Percentage Error between models and human judgments
● Insight: LLMs' inductive judgments on everyday phenomena under limited data don't
align with human cognition.

7. Analyzing the Discrepancies
● LLMs' Limitation: Failure in tasks requiring basic statistical principles
● Human Cognition: Relies on fewer parameters for similar tasks
● Observation: Discrepancy in LLMs' capability to make inductive judgments with limited
data.

8. Ethical Constraints in AI Responses
● Ethical Programming: Refusal to make predictions in sensitive tasks
● Impact: Ethical constraints influence AI decision-making, contrasting with human
cognition

9. Implications for AI Development
● Future Development: Need for AI to replicate nuances of human thought
● Cognitive Limitations: Recognizing and addressing gaps in AI's reasoning abilities

10. Closing Thoughts
● Summary: LLMs, despite linguistic abilities, fall short in cognitive judgments
● Importance: Recognizing limitations in AI's cognitive reasoning compared to human
cognition

11. Potential Future Impact
● Human-AI Collaboration:
Understanding the limitations of LLMs could improve how humans interact with and use
these models. For example, humans could take on tasks that require inductive reasoning,
while LLMs could handle more straightforward, data-intensive tasks.
● Ethical AI Design:
The paper may prompt discussions on how to program AI with ethical considerations. As
AI becomes more integrated into society, understanding the ethical boundaries within
which AI operates is crucial for developing responsible AI systems.

12. Thank you for
the attention