Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
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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. 2022.
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LLMs have been shown to excel at following instructions and comprehending retrieved passages.
“While LLMs have been shown to excel at these tasks (Wang et al., 2022;Zhong et al., 2023)”
From MedScore: Generalizable Factuality Evaluation of Open-ended Long-form Medical Answers by Domain-adapted Claim Decomposition and Verification (2026) · cites this paper as Wang et al., 2022
Section 6.1 (page unavailable) reports that instruction-tuned models outperform untuned models and learn to follow instructions on unseen tasks. The retrieved-passage comprehension part is not established: the paper lists question-answering and related tasks, but the passages found do not assess comprehension of retrieved passages specifically. (LLM, conf=0.86)
Experimental — accuracy not yet measuredAlpaca is based on LLAMA and fine-tuned on instructions data based on InstructGPT following the recipe from Wang et al. (2022).
“Alpaca (Taori et al., 2023) is based on LLAMA (Touvron et al., 2023) fine-tuned on the instructions data based on InstructGPT following the recipe from Wang et al. (2022).”
From FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation (2023) · cites this paper as Wang et al., 2022
The Heuristic baselines section discusses InstructGPT as a model evaluated by the authors, not as a source of instruction data for Alpaca. Searches for Alpaca, LLaMA, and an InstructGPT-based data-generation recipe found no passage in our copy addressing the attributed claim. (LLM, conf=0.75)
Experimental — accuracy not yet measuredThe current limited number of tasks may explain why the models have not yet exhibited emergent abilities to generalize to unseen clinical tasks.
“The current limited number of tasks may explain why we have not yet observed the models exhibiting emergent abilities to generalize to unseen clinical tasks, a capability seen in LLMs trained on thousands of diverse tasks in the general domain17,22.”
From Towards evaluating and building versatile large language models for medicine (2025) · cites this paper as [17]
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Towards building multilingual language model for medicine
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Self-citationLarge language models for reticular chemistry
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One Embedder, Any Task: Instruction-Finetuned Text Embeddings
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MEGA: Multilingual Evaluation of Generative AI
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Large language models: a survey of their development, capabilities, and applications
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Datasets for large language models: a comprehensive survey
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BLIVA: A Simple Multimodal LLM for Better Handling of Text-Rich Visual Questions
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Artificial intelligence co-piloted auditing
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Generative AI meets Responsible AI: Practical Challenges and Opportunities
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When brain-inspired AI meets AGI
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Unified-IO 2: Scaling Autoregressive Multimodal Models with Vision, Language, Audio, and Action
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Continual Learning of Large Language Models: A Comprehensive Survey
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Self-citationThe Revolution of Multimodal Large Language Models: A Survey
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“This technique, called instruction-tuning (Chung et al., 2022; Wang et al., 2022b,a; Jiang et al., 2024), turns out to be critical for aligning the behavior of an LLM with that of humans and currently empowers the most advanced LLMs, eventually boosted via reinforcement learning from human feedback…”
NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark
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A fine-grained comparison of pragmatic language understanding in humans and language models
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PromptCap: Prompt-Guided Image Captioning for VQA with GPT-3
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Evaluating Correctness and Faithfulness of Instruction-Following Models for Question Answering
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Orthogonal Subspace Learning for Language Model Continual Learning
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Multitask Vision-Language Prompt Tuning
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RenAIssance: A Survey Into AI Text-to-Image Generation in the Era of Large Model
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Understanding LLMs: A comprehensive overview from training to inference
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One-Shot Labeling for Automatic Relevance Estimation
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Large Language Model Instruction Following: A Survey of Progresses and Challenges
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Towards evaluating and building versatile large language models for medicine
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Self-citationA Review of Current Trends, Techniques, and Challenges in Large Language Models (LLMs)
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Recent Advances of Foundation Language Models-based Continual Learning: A Survey
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PUB: A Pragmatics Understanding Benchmark for Assessing LLMs’ Pragmatics Capabilities
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Examining Inter-Consistency of Large Language Models Collaboration: An In-depth Analysis via Debate
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Survey of Cultural Awareness in Language Models: Text and Beyond
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Task-aware Retrieval with Instructions
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Learning Customized Visual Models with Retrieval-Augmented Knowledge
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Data-Juicer: A One-Stop Data Processing System for Large Language Models
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INSTRUCTEVAL: Towards Holistic Evaluation of Instruction-Tuned Large Language Models
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Unicorn: A Unified Multi-tasking Model for Supporting Matching Tasks in Data Integration
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VIGC: Visual Instruction Generation and Correction
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EcomGPT: Instruction-Tuning Large Language Models with Chain-of-Task Tasks for E-commerce
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Generalization potential of large language models
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The Evolution of Large Language Model: Models, Applications and Challenges
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TARJAMAT: Evaluation of Bard and ChatGPT on Machine Translation of Ten Arabic Varieties
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Chain of Thought with Explicit Evidence Reasoning for Few-shot Relation Extraction
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RA-DIT: Retrieval-Augmented Dual Instruction Tuning
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MOSS: An Open Conversational Large Language Model
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CoEdIT: Text Editing by Task-Specific Instruction Tuning
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CREATOR: Tool Creation for Disentangling Abstract and Concrete Reasoning of Large Language Models
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Transfer learning for language model adaptation
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SGP-TOD: Building Task Bots Effortlessly via Schema-Guided LLM Prompting
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Instruct and Extract: Instruction Tuning for On-Demand Information Extraction
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A Survey of LLM Datasets: From Autoregressive Model to AI Chatbot
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Tc-llama 2: fine-tuning LLM for technology and commercialization applications
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So you want your private LLM at home? A survey and benchmark of methods for efficient GPTs
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On the potential of large language models to solve semantics-aware process mining tasks
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A Hybrid Multi-Agent Conversational Recommender System with LLM and Search Engine in E-commerce
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Dynosaur: A Dynamic Growth Paradigm for Instruction-Tuning Data Curation
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Demystifying Data Management for Large Language Models
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OctoPack: Instruction Tuning Code Large Language Models
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“Training models to follow instructions has led to new capabilities in text (Ouyang et al., 2022; Wang et al., 2022b; Chung et al., 2022) and visual modalities (Xu et al., 2023b; OpenAI, 2023).”
BackgroundMethodInfluentialUnderstanding the Effects of RLHF on LLM Generalisation and Diversity
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Towards Incremental Learning in Large Language Models: A Critical Review
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Agent Lumos: Unified and Modular Training for Open-Source Language Agents
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CoPrompter: User-Centric Evaluation of LLM Instruction Alignment for Improved Prompt Engineering
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Specialist or Generalist? Instruction Tuning for Specific NLP Tasks
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trlX: A Framework for Large Scale Reinforcement Learning from Human Feedback
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FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation
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Self-citationAffective computing in the era of large language models: A survey from the NLP perspective
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Exploring biases related to the use of large language models in a multilingual depression corpus
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Toward Human Readable Prompt Tuning: Kubrick’s The Shining is a good movie, and a good prompt too?
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KCTS: Knowledge-Constrained Tree Search Decoding with Token-Level Hallucination Detection
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C-STS: Conditional Semantic Textual Similarity
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LLM-Assisted Data Augmentation for Chinese Dialogue-Level Dependency Parsing
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Multi-view fusion for instruction mining of large language model
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Discrete Prompt Compression With Reinforcement Learning
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COSMO: A Large-Scale E-commerce Common Sense Knowledge Generation and Serving System at Amazon
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ProCyon: A multimodal foundation model for protein phenotypesPreprint
Queen, O., Huang, Y., Calef, R., Giunchiglia, V., Chen, T., Dasoulas, G., Tai, L., Abbadessa, G., Howell, O., Li, M. M., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.10.627665 · Cited by 13 · see citing works
Auto-Instruct: Automatic Instruction Generation and Ranking for Black-Box Language Models
Zhang, Z., Wang, S., Yu, W., Yi‐chong, X., Iter, D., Zeng, Q., Liu, Y., Zhu, C., & Jiang, M. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.659 · Cited by 15 · see citing works
- Authors
- Wang, Y., Mishra, S., Alipoormolabashi, P., Kordi, Y., Mirzaei, A., Naik, A., Ashok, A., Dhanasekaran, A. S., Arunkumar, A., Stap, D., Pathak, E., Karamanolakis, G., Lai, H., Purohit, I., Mondal, I., Anderson, J., Kuznia, K., Doshi, K., Pal, K. K., Patel, M., Moradshahi, M., Parmar, M., Purohit, M., Varshney, N., Kaza, P. R., Verma, P., Puri, R. S., Karia, R., Doshi, S., Sampat, S. K., Mishra, S., Reddy A, S., Patro, S., Dixit, T., & Shen, X.
- Affiliations
- University of Washington (US)
- University of Massachusetts Amherst (US)
- Sharif University of Technology (IR)
- Amirkabir University of Technology (IR)
- Indian Institute of Technology Kharagpur (IN)
- PSG College of Technology (IN)
- Arizona State University (US)
- University of Amsterdam (NL)
- University of California, Berkeley (US)
- Columbia University (US)
- Government Polytechnic College, Rajkot (IN)
- Microsoft (United States) (US)
- Stanford University (US)
- Tata Consultancy Services (India) (IN)
- Indian Institute of Technology Madras (IN)
- National University of Singapore (SG)
- ORCID
- Venue
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- Published
- 2022
- Pages
- 5085-5109
- OpenAlex
- W4385572845
- Cited by
- 247
- References
- 51
- Citations by year
- 2026: 37 · 2025: 58 · 2024: 64 · 2023: 88
- FWCI
- 29.01
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- Funders
- Arizona State University
- Defense Advanced Research Projects Agency (N66001-19-2-4031, N66001-19)
- Multidisciplinary University Research Initiative
- Office of Naval Research (N00014‐18‐1‐2670, N00014-18-1-2826, MURI N00014-18-1-2670, N00014)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- instruction tuning, large language models, natural language instructions
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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