From c7ff4c6a9731c101f921f1ebc8c6558f8bdd7704 Mon Sep 17 00:00:00 2001 From: Marek Kadlcik <10684818+markcheeky@users.noreply.github.com> Date: Tue, 3 Jan 2023 21:25:54 +0100 Subject: [PATCH] fix formatting on docs/supervised_data again to make linter happy again because the formatting rules changed in between --- docs/supervised_datasets.md | 66 ++++++++++++++++++++++++------------- 1 file changed, 44 insertions(+), 22 deletions(-) diff --git a/docs/supervised_datasets.md b/docs/supervised_datasets.md index c23e06f1..0f8c986d 100644 --- a/docs/supervised_datasets.md +++ b/docs/supervised_datasets.md @@ -1,55 +1,77 @@ # Supervised datasets -For discussion about usage of supervised data see issue . +For discussion about usage of supervised data see issue +. ## Motivation -An important part of making the assistant useful is to teach it to understand and follow instructions, and to perform large set of tasks well. +An important part of making the assistant useful is to teach it to understand +and follow instructions, and to perform large set of tasks well. -While RLHF seems like the main ingredient, using existing supervised data might help. +While RLHF seems like the main ingredient, using existing supervised data might +help. -There are two large-scale projects in the area of instruction-following / multitask learning: Promptsource and Natural Instructions - -these projects crowdsourced templates and turned existing NLP datasets into instruction-following seq2seq form in natural langauge. -They include both long-output training examples like generating a sentence that is a likely consequence of sentence in the prompt, and -short-output, like rating prediction from review. (Pre-)training on such datasets should help model understand and follow instructions -and teach it many abilities neccessary to perform a large set of tasks correctly. However, these data are not dialog-like - they do not +There are two large-scale projects in the area of instruction-following / +multitask learning: Promptsource and Natural Instructions - these projects +crowdsourced templates and turned existing NLP datasets into +instruction-following seq2seq form in natural langauge. They include both +long-output training examples like generating a sentence that is a likely +consequence of sentence in the prompt, and short-output, like rating prediction +from review. (Pre-)training on such datasets should help model understand and +follow instructions and teach it many abilities neccessary to perform a large +set of tasks correctly. However, these data are not dialog-like - they do not look like a normal conversation. -There are also supervised dialog datasets such as Blended Skill Talk or SODA. In constrast to instruction-following datasets, dialog data -is not as focused on "academic tasks" or correctness, but encourage the model to respond naturally like a person would. +There are also supervised dialog datasets such as Blended Skill Talk or SODA. In +constrast to instruction-following datasets, dialog data is not as focused on +"academic tasks" or correctness, but encourage the model to respond naturally +like a person would. ### Promptsource - GitHub: -- paper: [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://arxiv.org/abs/2110.08207) +- paper: + [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://arxiv.org/abs/2110.08207) - project for preparing templates and working with them - they generated a dataset using the templates: - - - (with multilingual data but English prompt) - - (with multilingual data and machine-translated prompt) -- they trained zero-shot models (= models for following instructions in the input) - - based on T5 architecture (encoder-decoder) called T0 family (and MT0 for multilingual) + - (with multilingual data but + English prompt) + - (with multilingual data + and machine-translated prompt) +- they trained zero-shot models (= models for following instructions in the + input) + - based on T5 architecture (encoder-decoder) called T0 family (and MT0 for + multilingual) - and based on GPT architecture (decoder-only) called BloomZ family - - Huggingface demo: [T0](https://huggingface.co/bigscience/T0pp), [MT0](https://huggingface.co/bigscience/mt0-large), [BloomZ](https://huggingface.co/bigscience/bloomz), + - Huggingface demo: [T0](https://huggingface.co/bigscience/T0pp), + [MT0](https://huggingface.co/bigscience/mt0-large), + [BloomZ](https://huggingface.co/bigscience/bloomz), - GitHub repo for T0: - - GitHub repo for BloomZ and MT0: + - GitHub repo for BloomZ and MT0: + ### Natural instructions - GitHub: -- paper: [Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks](https://arxiv.org/abs/2204.07705) -- they crowdsource directly the data prepared for instruction following (and learning from a few examples) +- paper: + [Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks](https://arxiv.org/abs/2204.07705) +- they crowdsource directly the data prepared for instruction following (and + learning from a few examples) - the GitHub repo = the dataset. It contains jsons - they trained zero-shot and in-context few-shot models (in multiple sizes): - mT5 architecture (encoder-decoder, multilingual pretraining) - - Huggingface demo few-shot: - - Huggingface demo zero-shot: + - Huggingface demo few-shot: + + - Huggingface demo zero-shot: + ### Blended Skill Talk - used by Facebook in Blenderbot project - HuggingFace dataset: -- example model trained on it: +- example model trained on it: + ### SODA