Large-Scale Pre-Training for Goal-Directed Dialog (GODEL)
GODEL is a large-scale pre-trained model for goal-directed dialogs. It is parameterized with a Transformer-based encoder-decoder model and trained for response generation grounded in external text, which allows more effective fine-tuning on dialog tasks that require conditioning the response on information that is external to the current conversation (e.g., a retrieved document). The pre-trained model can be efficiently fine-tuned and adapted to accomplish a new dialog task with a handful of task-specific dialogs. The v1.1 model is trained on 551M multi-turn dialogs from Reddit discussion thread, and 5M instruction and knowledge grounded dialogs.
Multi-turn generation examples from an interactive environment:
Chitchat example:
Instruction: given a dialog context, you need to response empathically. User: Does money buy happiness? Agent: It is a question. Money buys you a lot of things, but not enough to buy happiness. User: What is the best way to buy happiness ? Agent: Happiness is bought through your experience and not money.
Grounded response generation example:
Instruction: given a dialog context and related knowledge, you need to response safely based on the knowledge. Knowledge: The best Stardew Valley mods PCGamesN_0 / About SMAPI User: My favorite game is stardew valley. stardew valley is very fun. Agent: I love Stardew Valley mods, like PCGamesN_0 / About SMAPI.
Please find the information about preprocessing, training and full details of the GODEL in the project webpage.
ArXiv paper: https://arxiv.org/abs/2206.11309
How to use
Now we are ready to try out how the model works as a chatting partner!
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("microsoft/GODEL-v1_1-large-seq2seq")
model = AutoModelForSeq2SeqLM.from_pretrained("microsoft/GODEL-v1_1-large-seq2seq")
def generate(instruction, knowledge, dialog):
if knowledge != '':
knowledge = '[KNOWLEDGE] ' + knowledge
dialog = ' EOS '.join(dialog)
query = f"{instruction} [CONTEXT] {dialog} {knowledge}"
input_ids = tokenizer(f"{query}", return_tensors="pt").input_ids
outputs = model.generate(input_ids, max_length=128, min_length=8, top_p=0.9, do_sample=True)
output = tokenizer.decode(outputs[0], skip_special_tokens=True)
return output
# Instruction for a chitchat task
instruction = f'Instruction: given a dialog context, you need to response empathically.'
# Leave the knowldge empty
knowledge = ''
dialog = [
'Does money buy happiness?',
'It is a question. Money buys you a lot of things, but not enough to buy happiness.',
'What is the best way to buy happiness ?'
]
response = generate(instruction, knowledge, dialog)
print(response)
Citation
if you use this code and data in your research, please cite our arxiv paper:
@misc{peng2022godel,
author = {Peng, Baolin and Galley, Michel and He, Pengcheng and Brockett, Chris and Liden, Lars and Nouri, Elnaz and Yu, Zhou and Dolan, Bill and Gao, Jianfeng},
title = {GODEL: Large-Scale Pre-training for Goal-Directed Dialog},
howpublished = {arXiv},
year = {2022},
month = {June},
url = {https://www.microsoft.com/en-us/research/publication/godel-large-scale-pre-training-for-goal-directed-dialog/},
}