2022年10月20日 · Flan-PaLM 540B achieves state-of-the-art performance on several benchmarks, such as 75.2% on five-shot MMLU. We also publicly release Flan-T5 checkpoints, which achieve strong few-shot performance even compared to …

论文使用指令微调方式训练了Flan-PaLM，模型规模参数增加到540B, 微调任务数达1.8k, 并在微调任务中增加了推理链微调，整体结论如下. Scaling Instruction-Finetuned Language Models核心内容论文围绕"instruction fine-tuning"重点做了以下优化内容： 扩大任务数量 【FLAN提到过任务多样性会提升效果】扩大模型规模参数【LLM规模越大, in-…

指令微调的 Flan-PaLM 模型以计算高效的方式扩展，参数量扩展到 540B 参数语言模型，任务扩展到 1.8K 微调任务，并在微调中包括思想链 (CoT) 数据。 Flan-PaLM 在多个基准测试中实现了最先进的性能，例如在五次 MMLU 上达到 75.2%。 Flan-PaLM 还改进了可用性。 参考文章

Flan-PaLM 540B achieves state-of-the-art performance on several benchmarks, such as 75.2% on five-shot MMLU. We also publicly release Flan-T5 checkpoints,1 which achieve strong few-shot performance even compared to much larger models, such as PaLM 62B.

2023年2月10日 · This paper presents very large scale instruction fine-tuning performed on T5 and PaLM models. Comparing instruction finetuned Flan-PaLM vs pretrained PaLM (of different sizes: 8B, 62B, 540B), multi-task instruction finetuning significantly improves performance compared to …

使用 540B 参数模型训练 Flan-PaLM，将微调任务的数量增加到 1.8K，包括 CoT 数据。 Flan-PaLM 优于 PaLM，在几个基准测试中实现了最优效果。 比如 MMLU 实现了 75.2 的精度. 指令微调 Flan-T5 模型（80M 到 11B）。 这些 checkpoints 具有很强的 zeroshot、few-shot 和 CoT 能力，优于之前的 T5 模型。 将指令微调称为 Flan（Finetuning language models），加上 Flan 的模型指代微调后的模型，比如 Flan-PaLM。 指令微调的流程可适配以下多种模型结构. 指令微调对 …

All of the models will be further instruction-tuned on FLAN to provide flan-PaLM models. The models were trained with Flash Attention, Xpos Rotary Embeddings for better length extrapolation, and multi-query single-key-value attention for more efficient decoding.

The first is the original Flan 2021, documented in Finetuned Language Models are Zero-Shot Learners, and the second is the expanded version, called the Flan Collection, described in The Flan Collection: Designing Data and Methods for Effective Instruction Tuning and used to produce Flan-T5 and Flan-PaLM.

2023年7月12日 · We propose a human evaluation framework for model answers along multiple axes including factuality, comprehension, reasoning, possible harm and bias. In addition, we evaluate Pathways Language...

2022年12月26日 · We propose a framework for human evaluation of model answers along multiple axes including factuality, precision, possible harm, and bias. In addition, we evaluate PaLM (a 540-billion parameter LLM) and its instruction-tuned variant, Flan-PaLM, on MultiMedQA.