Aristo

Building the next generation of systems that can systematically reason, explain, and continually improve over time

Diagram showing entailment tree from hypothesis and text

Our research includes pioneering work on:

Systematic reasoning and explanation
Teachable reasoning systems
Continual learning with memory-based architectures
Knowledge and belief
Universal mathematical reasoning

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.

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Neuro-Symbolic Reasoning and Explanation

Solving problems by generating consistent, faithful chains of reasoning using neural components.

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Modular Models

By learning to chain together existing models, complex problems can be solved, beyond the capabilities of the individual components.

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Universal Mathematical Reasoners

Creating models with built-in mathematical reasoning skills, that can be rapidly fine-tuned for a wide variety of mathematical tasks.

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Demos

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Macaw

A QA model that outperforms other popular language models while being an order of magnitude smaller | Aristo

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.

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Macaw

A QA model that outperforms other popular language models while being an order of magnitude smaller | Aristo

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ProofWriter

Generating Implications, Proofs, and Abductive Statements over Natural Language | Aristo

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.

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ProofWriter

Generating Implications, Proofs, and Abductive Statements over Natural Language | Aristo

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.

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Recent Papers

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DREAM: Improving Situational QA by First Elaborating the Situation
Yuling Gu, Bhavana Dalvi Mishra, Peter ClarkNAACL 2021 • 2022 When people answer questions about a specific situation, e.g., "I cheated on my mid-term exam last week. Was that wrong?", cognitive science suggests that they form a mental picture of that situation before answering. While we do not know how language models…
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Cross-Task Generalization via Natural Language Crowdsourcing Instructions
Swaroop Mishra, Daniel Khashabi, Chitta Baral, Hanna HajishirziACL • 2022 Can we enable NLP models to appropriately respond to instructional prompts and consequently generalize to new tasks? To study this question, we leverage the existing NLP datasets and the instructions that were used to crowdsource them to create…
Hey AI, Can You Solve Complex Tasks by Talking to Agents?
Tushar Khot, Kyle Richardson, Daniel Khashabi, Ashish SabharwalFindings of ACL • 2022 Humans often solve complex problems by interacting (in natural language) with existing agents, such as AI assistants, that can solve simpler sub-tasks. These agents themselves can be powerful systems built using extensive resources and privately held data. In…
Saturated Transformers are Constant-Depth Threshold Circuits
William Cooper Merrill, Ashish Sabharwal, Noah A. SmithTACL • 2022 Transformers have become a standard neural network architecture for many NLP problems, motivating theoretical analysis of their power in terms of formal languages. Recent work has shown that transformers with *hard* attention are quite limited in power, as…
What Makes Instruction Learning Hard? An Investigation and a New Challenge in a Synthetic Environment
Matthew Finlayson, Kyle Richardson, Ashish Sabharwal, Peter ClarkarXiv • 2022 The instruction learning paradigm—where a model learns to perform new tasks from task descriptions alone—has become popular in general-purpose model research. The capabilities of large transformer models as instruction learners, however, remain poorly under…
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Recent Datasets

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Multihop Questions via Single-hop Question Composition

Multihop reading comprehension dataset with 2-4 hop questions.

MuSiQue is a multihop reading comprehension dataset with 2-4 hop questions, built by composing seed questions from 5 existing single-hop datasets. The dataset is constructed with a bottom-up approach that systematically selects composable pairs of single-hop questions that are connected, i.e., where one reasoning step requires information from the other. This approach allows greater control over the properties of the resulting k-hop questions, allowing us to create a dataset that is substantially less cheatable (e.g. by shortcut-based or singlehop reasoning) and more challenging than prior similar datasets. MuSiQue comes in two variations -- MuSiQue-Answerable, which contains only answerable questions, and MuSiQue-Full, which contains both answerable and unanswerable questions. In the latter, each answerable question from MuSiQue-Answerable is paired with closely similar unanswerable question. In MuSiQue-Answerable, the task is to identify the answer and the supporting paragraphs, given a question and a context of up to 20 paragraphs. In MuSiQue-Full, the task is to first determine whether the question is answerable from the given context, and if it is, identify the answer and the supporting paragraphs.

“Knowing is not enough, we must apply. Willing is not enough, we must do.”

— Johann Wolfgang von Goethe