Scaling Instruction-Finetuned Language Models
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This paper explores the benefits scaling instruction finetuning (opens in a new tab) and how it improves performance on a variety of models (PaLM, T5), prompting setups (zero-shot, few-shot, CoT), and benchmarks (MMLU, TyDiQA). This is explored with the following aspects: scaling the number of tasks (1.8K tasks), scaling model size, and finetuning on chain-of-thought data (9 datasets used).
Finetuning procedure:
1.8K tasks were phrased as instructions and used to finetune the model
Uses both with and without exemplars, and with and without CoT
Finetuning tasks and held out tasks shown below:
Instruction finetuning scales well with the number of tasks and the size of the model; this suggests the need for scaling number of tasks and size of model further
Adding CoT datasets into the finetuning enables good performance on reasoning tasks
Flan-PaLM has improved multilingual abilities; 14.9% improvement on one-shot TyDiQA; 8.1% improvement on arithmetic reasoning in under-represented languages
Plan-PaLM also performs well on open-ended generation questions, which is a good indicator for improved usability
Improves performance across responsible AI (RAI) benchmarks
Flan-T5 instruction tuned models demonstrate strong few-shot capabilities and outperforms public checkpoint such as T5
The results when scaling number of finetuning tasks and model size: scaling both the size of the model and the number of finetuning tasks is expected to continue improving performance, although scaling the number of tasks has diminished returns.
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The results when finetuning with non-CoT and CoT data: Jointly finetuning on non-CoT and CoT data improves performance on both evaluations, compared to finetuning on just one or the other.
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In addition, self-consistency combined with CoT achieves SoTA results on several benchmarks. CoT + self-consistency also significantly improves results on benchmarks involving math problems (e.g., MGSM, GSM8K).
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CoT finetuning unlocks zero-shot reasoning, activated by the phrase "let's think step-by-step", on BIG-Bench tasks. In general, zero-shot CoT Flan-PaLM outperforms zero-shot CoT PaLM without finetuning.
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Below are some demonstrations of zero-shot CoT for PaLM and Flan-PaLM in unseen tasks.
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Below are more examples for zero-shot prompting. It shows how the PaLM model struggles with repetitions and not replying to instructions in the zero-shot setting where the Flan-PaLM is able to perform well. Few-shot exemplars can mitigate these errors.
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Below are some examples demonstrating more zero-shot capabilities of the Flan-PALM model on several different types of challenging open-ended questions:
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You can try Flan-T5 models on the Hugging Face Hub (opens in a new tab).
