Yeganeh Kordi


2023

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Self-Instruct: Aligning Language Models with Self-Generated Instructions
Yizhong Wang | Yeganeh Kordi | Swaroop Mishra | Alisa Liu | Noah A. Smith | Daniel Khashabi | Hannaneh Hajishirzi
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Large “instruction-tuned” language models (i.e., finetuned to respond to instructions) have demonstrated a remarkable ability to generalize zero-shot to new tasks. Nevertheless, they depend heavily on human-written instruction data that is often limited in quantity, diversity, and creativity, therefore hindering the generality of the tuned model. We introduce Self-Instruct, a framework for improving the instruction-following capabilities of pretrained language models by bootstrapping off their own generations. Our pipeline generates instructions, input, and output samples from a language model, then filters invalid or similar ones before using them to finetune the original model. Applying our method to the vanilla GPT3, we demonstrate a 33% absolute improvement over the original model on Super-NaturalInstructions, on par with the performance of InstructGPT-001, which was trained with private user data and human annotations. For further evaluation, we curate a set of expert-written instructions for novel tasks, and show through human evaluation that tuning GPT3 with Self-Instruct outperforms using existing public instruction datasets by a large margin, leaving only a 5% absolute gap behind InstructGPT-001. Self-Instruct provides an almost annotation-free method for aligning pre-trained language models with instructions, and we release our large synthetic dataset to facilitate future studies on instruction tuning.

2022

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Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
Yizhong Wang | Swaroop Mishra | Pegah Alipoormolabashi | Yeganeh Kordi | Amirreza Mirzaei | Atharva Naik | Arjun Ashok | Arut Selvan Dhanasekaran | Anjana Arunkumar | David Stap | Eshaan Pathak | Giannis Karamanolakis | Haizhi Lai | Ishan Purohit | Ishani Mondal | Jacob Anderson | Kirby Kuznia | Krima Doshi | Kuntal Kumar Pal | Maitreya Patel | Mehrad Moradshahi | Mihir Parmar | Mirali Purohit | Neeraj Varshney | Phani Rohitha Kaza | Pulkit Verma | Ravsehaj Singh Puri | Rushang Karia | Savan Doshi | Shailaja Keyur Sampat | Siddhartha Mishra | Sujan Reddy A | Sumanta Patro | Tanay Dixit | Xudong Shen
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

How well can NLP models generalize to a variety of unseen tasks when provided with task instructions? To address this question, we first introduce Super-NaturalInstructions, a benchmark of 1,616 diverse NLP tasks and their expert-written instructions. Our collection covers 76 distinct task types, including but not limited to classification, extraction, infilling, sequence tagging, text rewriting, and text composition. This large and diverse collection of tasks enables rigorous benchmarking of cross-task generalization under instructions—training models to follow instructions on a subset of tasks and evaluating them on the remaining unseen ones. Furthermore, we build Tk-Instruct, a transformer model trained to follow a variety of in-context instructions (plain language task definitions or k-shot examples). Our experiments show that Tk-Instruct outperforms existing instruction-following models such as InstructGPT by over 9% on our benchmark despite being an order of magnitude smaller. We further analyze generalization as a function of various scaling parameters, such as the number of observed tasks, the number of instances per task, and model sizes. We hope our dataset and model facilitate future progress towards more general-purpose NLP models.