Bibliography (24):

1. GPT-3: Language Models are Few-Shot Learners

2. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

3. https://arxiv.org/pdf/2210.03350#page=17&org=allen

4. Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps

5. MuSiQue: Multi-hop Questions via Single-hop Question Composition

6. Large Language Models are Zero-Shot Reasoners

7. InstructGPT: Training language models to follow instructions with human feedback

8. https://arxiv.org/pdf/2210.03350.pdf#page=24

9. RealTime QA: What’s the Answer Right Now?

10. https://serpapi.com/

11. https://arxiv.org/pdf/2210.03350#page=24&org=allen

12. Large Language Models Can Self-Improve

13. Language Model Cascades

14. Self-Consistency Improves Chain-of-Thought Reasoning in Language Models

15. Language Models (Mostly) Know What They Know

16. Boosting Theory-of-Mind Performance in Large Language Models via Prompting

17. Ask Me Anything (AMA): A simple strategy for prompting language models

18. What Can a Generative Language Model Answer About a Passage?

19. Language Models are Multilingual Chain-of-Thought Reasoners

20. Predictability and Surprise in Large Generative Models

21. WebGPT: Improving the factual accuracy of language models through web browsing

22. Wikipedia Bibliography:

    1. Plato  :

       https://en.wikipedia.org/wiki/Plato

    2. https://en.wikipedia.org/wiki/.gr  :

       https://en.wikipedia.org/wiki/.gr

    3. Statistical power