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Keyword: instruction tuning ×Clear all

5 claims from 3 papers

1. Computer Science › Topic Modeling

   Scaling Instruction-Finetuned Language Models

   Chung, Le Hou, Longpre et al. · arXiv (Cornell University) · 2022

   The paper studies instruction finetuning of language models, scaling the number of tasks, model size and chain-of-thought data, and reports large gains across several model families and benchmarks.

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   1. UncheckedFlan-PaLM 540B, instruction-finetuned on 1.8K tasks, scores 9.4% higher on average than the original PaLM 540B.“For instance, Flan-PaLM 540B instruction-finetuned on 1.8K tasks outperforms PALM 540B by a large margin (+9.4% on average).”
   2. UncheckedFlan-PaLM 540B reaches state-of-the-art results on several benchmarks, including 75.2% on the five-shot MMLU test.“Flan-PaLM 540B achieves state-of-the-art performance on several benchmarks, such as 75.2% on five-shot MMLU.”
   3. UncheckedInstruction finetuning that scales tasks, model size and chain-of-thought data greatly improves results across several model families, prompting styles and benchmarks.“We find that instruction finetuning with the above aspects dramatically improves performance on a variety of model classes (PaLM, T5, U-PaLM), prompting setups (zero-shot, few-shot, CoT), and evaluation benchmarks (MMLU, BBH, TyDiQA, MGSM, open-ended generation).”

2. Computer Science › Multimodal Machine Learning Applications

   Visual Instruction Tuning

   Liu, Li, Wu and Lee · arXiv (Cornell University) · 2023

   The authors use language-only GPT-4 to generate image-and-text instruction data, then train LLaVA, a model linking a vision encoder to an LLM, and report chat ability and benchmark results.

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   1. UncheckedAfter fine-tuning on the Science QA benchmark, combining LLaVA with GPT-4 reaches 92.53% accuracy, which the authors call a new state of the art.“When fine-tuned on Science QA, the synergy of LLaVA and GPT-4 achieves a new state-of-the-art accuracy of 92.53%.”

3. Medicine › Artificial Intelligence in Healthcare and Education

   Radiology-GPT: A Large Language Model for Radiology

   Liu, Zhong, Li et al. · arXiv (Cornell University) · 2023

   The authors introduce Radiology-GPT, a radiology-specific large language model, and report that it outperforms general models and could suit privacy-compliant, hospital-specific use.

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   1. UncheckedRadiology-GPT, a language model tuned on radiology knowledge, is reported to outperform general models such as StableLM, Dolly and LLaMA.“Using an instruction tuning approach on an extensive dataset of radiology domain knowledge, Radiology-GPT demonstrates superior performance compared to general language models such as StableLM, Dolly and LLaMA.”