Chain-of-thought prompting involves dividing complex reasoning tasks into natural language steps to help large language models perform better. It has been shown to improve arithmetic word problem solving by prompting models to show the steps and equations used to arrive at the answer. An ablation study found that showing the intermediate steps led to better performance than just showing the equation or computed answer alone. While promising for improving reasoning abilities, chain-of-thought prompting may not truly elicit human-like reasoning and can be costly to apply due to annotation efforts and model sizes required.

1. Using Chain-of-thought
prompting to elicit reasoning
in Large Language Models
Neetha Sherra
San Jose State University
CMPE 258-Deep Learning

2. Introduction
• The recent growth of Language Models (LM’s) in NLP has been
ground breaking especially with respect to Large Language
Models
• LLM’s are used across a range of natural language tasks
• Some of the broad benefits of scaling LM’s include improved
performance, generalization and efficiency
• Scaling is necessary for all tasks but is not sufficient for certain
tasks

3. Chain-of-thought prompting
• What is chain-of-thought
• Combining two approaches
• Can be trained to generate intermediate natural language steps
• Have been successful at question-answering with few-shot prompting
• What are the benefits of chain of thought prompting
• Division into steps
• Internal view
• Turning a general LLM into one that performs complex tasks

4. Arithmetic reasoning
• X-axis: model scale, y-axis: math
word problem benchmarks
• A large model is necessary but
not sufficient
• Performance and problem
complexity
• Performances beats previous
SOTA

5. Arithmetic reasoning continued…
• Ablation study
• Equation only
• Variable compute only
• Chain-of-thought after answer

6. Arithmetic reasoning continued…
• Robustness of chain-of-thought
prompting

7. Other reasoning tasks
Left: Commonsense reasoning performance of PaLM, Right:
Symbolic reasoning performance of PaLM

8. Limitations
• Is it really “reasoning”?
• Cost of annotation
• “right” reasoning path
• Cost of employing a ‘large’ language model
Conclusions
• Broadening the range of tasks
• Decreasing the scale threshold

9. References
https://arxiv.org/pdf/2201.11903.pdf

10. Thank You