Aristo
Building the next generation of systems that can systematically reason, explain, and continually improve over time
- Systematic reasoning and explanation
- Teachable reasoning systems
- Continual learning with memory-based architectures
- Knowledge and belief
- Universal mathematical reasoning
Recent Updates
Towards Teachable Reasoning Systems
April 27, 2022This paper describes our work towards Teachable Reasoning Systems. First, EntailmentWriter searches for a chain of reasoning from facts it believes…
Memory-assisted prompt editing to improve GPT-3 after deployment
April 20, 2022Large LMs such as GPT-3 are powerful, but can commit mistakes that are obvious to humans. Memory-assisted prompt editing allows users to give…
DREAM: Improving Situational QA by First Elaborating the Situation
March 1, 2022When people answer questions about a specific situation, e.g., "I cheated on my mid-term exam last week. Was that wrong?", cognitive science suggests…
Explaining Answers with Entailment Trees
November 1, 2021EntailmentBank is a unique dataset of multi-step entailment trees. Each tree shows how known facts combine to entail the answer to a question. From…
BeliefBank: Adding Memory to a Pre-Trained Language Model for a Systematic Notion of Belief
November 1, 2021Although pretrained language models (PTLMs) contain significant amounts of world knowledge, they can still produce inconsistent answers to questions…
Research Areas
Teachable Reasoning Systems
By interacting with and giving feedback on a system’s reasoning, a user can teach the system so it continually improves over time – without model retraining.
Neuro-Symbolic Reasoning and Explanation
Solving problems by generating consistent, faithful chains of reasoning using neural components.
Modular Models
By learning to chain together existing models, complex problems can be solved, beyond the capabilities of the individual components.
Universal Mathematical Reasoners
Creating models with built-in mathematical reasoning skills, that can be rapidly fine-tuned for a wide variety of mathematical tasks.
Macaw is a high-performance question-answering (QA) model capable of outperforming other popular current language models, all while being an order of magnitude smaller. This demo allows you to explore Macaw's answers and compare them to those of the popular GPT-3 language model on a benchmark set of questions.
Try the demo
Macaw is a high-performance question-answering (QA) model capable of outperforming other popular current language models, all while being an order of magnitude smaller. This demo allows you to explore Macaw's answers and compare them to those of the popular GPT-3 language model on a benchmark set of questions.
Try the demo
Like RuleTaker, ProofWriter determines whether statements are True or False based on rules given in natural language - but also generates the proof of its answers.
Try the demoLike RuleTaker, ProofWriter determines whether statements are True or False based on rules given in natural language - but also generates the proof of its answers.
Try the demoRecent Papers
Improving Language Model Negotiation with Self-Play and In-Context Learning from AI Feedback
Yao Fu, Hao-Chun Peng, Tushar Khot, Mirella LapataarXiv.org • 2023 We study whether multiple large language models (LLMs) can autonomously improve each other in a negotiation game by playing, reflecting, and criticizing. We are interested in this question because if LLMs were able to improve each other, it would imply the…Complexity-Based Prompting for Multi-Step Reasoning
Yao Fu, Hao-Chun Peng, Ashish Sabharwal, Peter Clark, Tushar KhotICLR • 2023 We study the task of prompting large-scale language models to perform multi-step reasoning. Existing work shows that when prompted with a chain of thoughts (CoT), sequences of short sentences describing intermediate reasoning steps towards a final answer…Decomposed Prompting: A Modular Approach for Solving Complex Tasks
Tushar Khot, Harsh Trivedi, Matthew Finlayson, Yao Fu, Kyle Richardson, Peter Clark, Ashish SabharwalICLR • 2023 Few-shot prompting is a surprisingly powerful way to use Large Language Models (LLMs) to solve various tasks. However, this approach struggles as the task complexity increases or when the individual reasoning steps of the task themselves are hard to learn…Transformers Can Be Expressed In First-Order Logic with Majority
William Merrill, Ashish SabharwalarXiv • 2023 Characterizing the implicit structure of the computation within neural networks is a foundational problem in the area of deep learning interpretability. Can the inner decision process of neural networks be captured symbolically in some familiar logic? We show…Do language models have coherent mental models of everyday things?
Yuling Gu, Bhavana Dalvi Mishra, Peter ClarkarXiv • 2022 When people think of everyday things like an “egg,” they typically have a mental image associated with it. This commonsense knowledge helps us understand how these everyday things work and how to interact with them. For example, when someone tries to make a…
Recent Datasets
Belief and Reasoning Dataset
BaRDA: A Belief and REasoning Dataset that Separates Factual Accuracy and Reasoning Ability
BaRDa is a new belief and reasoning dataset for evaluating the factual correctness ("truth") and reasoning accuracy ("rationality", or "honesty") of new language models. It was created in collaboration with, and with the support of, the Open Philanthropy organization.
Lila
A math reasoning benchmark of over 140K natural language questions annotated with Python programs
A comprehensive benchmark for mathematical reasoning with over 140K natural language questions annotated with Python programs and natural language instructions. The data set comes with multiple splits: Lila-IID (train, dev, test), Lila-OOD (train, dev, test), and Lila-Robust.
Entailer
Data for "Entailer: Answering Questions with Faithful and Truthful Chains of Reasoning", EMNLP 2022
Data for "Entailer: Answering Questions with Faithful and Truthful Chains of Reasoning", EMNLP 2022
TeachMe
Supplementary data for "Towards Teachable Reasoning Systems: Using a Dynamic Memory ...", EMNLP 2022
Supplementary data for "Towards Teachable Reasoning Systems: Using a Dynamic Memory ...", EMNLP 2022
Recent Press
This AI Paper Shows How ChatGPT’s Toxicity Can Increase Up To Six-Fold When Assigned A Persona
April 14, 2023
'They’re All So Dirty and Smelly:' Study Unlocks ChatGPT's Inner Racist
April 13, 2023
ChatGPT can turn toxic just by changing its assigned persona, researchers say
April 12, 2023
Researchers discover a way to make ChatGPT consistently toxic
April 12, 2023
Researchers From Allen Institute for AI Introduce TeachMe: A Framework To Understand And Correct AI Models
January 17, 2023
Allen Institute for Artificial Intelligence Introduces MemPrompt: A New Method to “fix” GPT-3 After Deployment with User Interaction
December 18, 2022
Researchers at Allen Institute for AI Built a System Called DREAM-FLUTE to Explore Machine Learning ‘Mental Models’ for Figurative Language
December 1, 2022
Researchers at the Allen Institute for AI Propose Līla, a Unified Benchmark for Comprehensive Evaluation of the Mathematical Reasoning Abilities of Artificial Intelligence Systems
November 14, 2022
Team
Peter ClarkInterim Chief Executive Officer
Bhavana DalviResearch
Matt FinlaysonPredoctoral Young Investigator
Yuling GuPredoctoral Young InvestigatorAshwin KalyanResearch
Tushar KhotResearch
Kyle RichardsonResearch
Ashish SabharwalResearch
Oyvind TafjordResearch
Niket TandonResearch
Sarah WiegreffeYoung Investigator
