Scaling Instruction-Finetuned Language Models
 
This paper discusses how scaling up instruction finetuning can enhance the performance of various language models like PaLM and T5. It examines different prompting methods (like zero-shot and few-shot learning) and evaluates their effectiveness using benchmarks like MMLU and TyDiQA. Key areas of focus include:

- Increasing the number of tasks to 1,800
- Expanding model size
- Finetuning using chain-of-thought (CoT) data from nine different datasets

Finetuning Process  
The model was finetuned using 1,800 tasks, framed as instructions. This process involved different approaches, both with examples and without, as well as incorporating CoT methods.

Key Findings 

1. Scaling Benefits: Instruction finetuning works better as you increase the number of tasks and the size of the model. This indicates that further scaling could yield even better results.
  
2. CoT Data: Including CoT datasets during finetuning helps improve performance on reasoning tasks.

3. Multilingual Improvements: Flan-PaLM shows enhanced capabilities in multiple languages, achieving a 14.9% improvement on one-shot TyDiQA and an 8.1% improvement in arithmetic reasoning for under-represented languages.

4. Open-ended Generation: Flan-PaLM also performs well on open-ended questions, indicating better usability.

5. Responsible AI Performance: It shows improvements across responsible AI benchmarks.

6. Few-shot Capabilities: The instruction-tuned Flan-T5 models demonstrate strong few-shot learning capabilities, outperforming public versions like T5.

7. Scaling Observations: Increasing the number of finetuning tasks and model size is expected to continue enhancing performance, but adding too many tasks may lead to diminishing returns.

CoT and Non-CoT Data:  
Finetuning the model using both non-CoT and CoT data together enhances performance on evaluations compared to using either type alone.

Self-Consistency:  
Combining self-consistency with CoT achieves state-of-the-art results on several benchmarks, especially those involving math problems.

Zero-shot Reasoning:  
Finetuning with CoT enables zero-shot reasoning when using the phrase "let's think step-by-step" on BIG-Bench tasks. In general, zero-shot CoT with Flan-PaLM outperforms zero-shot CoT with PaLM that hasn't been finetuned.

Examples and Demonstrations:  
The paper includes demonstrations showcasing zero-shot CoT capabilities of both PaLM and Flan-PaLM on unseen tasks. It highlights how Flan-PaLM handles complex open-ended questions better than PaLM, which often struggles with repetitive responses and instructions in zero-shot situations. Additionally, few-shot examples can help reduce these errors.