b'Peter Clark'

research

∙ 05/24/2023

Attentiveness to Answer Choices Doesn't Always Entail High QA Accuracy

When large language models (LMs) are applied in zero- or few-shot settin...

0 Sarah Wiegreffe, et al. ∙

research

∙ 05/23/2023

Language Models with Rationality

While large language models (LLMs) are proficient at question-answering ...

0 Nora Kassner, et al. ∙

research

∙ 05/23/2023

IfQA: A Dataset for Open-domain Question Answering under Counterfactual Presuppositions

Although counterfactual reasoning is a fundamental aspect of intelligenc...

0 Wenhao Yu, et al. ∙

research

∙ 05/22/2023

Let GPT be a Math Tutor: Teaching Math Word Problem Solvers with Customized Exercise Generation

In this paper, we present a novel approach for distilling math word prob...

0 Zhenwen Liang, et al. ∙

research

∙ 05/15/2023

RL4F: Generating Natural Language Feedback with Reinforcement Learning for Repairing Model Outputs

Despite their unprecedented success, even the largest language models ma...

0 Afra Feyza Akyürek, et al. ∙

research

∙ 03/30/2023

Self-Refine: Iterative Refinement with Self-Feedback

Like people, LLMs do not always generate the best text for a given gener...

2 Aman Madaan, et al. ∙

research

∙ 12/20/2022

Do language models have coherent mental models of everyday things?

When people think of everyday things like an "egg," they typically have ...

15 Yuling Gu, et al. ∙

research

∙ 10/31/2022

Lila: A Unified Benchmark for Mathematical Reasoning

Mathematical reasoning skills are essential for general-purpose intellig...

15 Swaroop Mishra, et al. ∙

research

∙ 10/28/2022

Just-DREAM-about-it: Figurative Language Understanding with DREAM-FLUTE

Figurative language (e.g., "he flew like the wind") is challenging to un...

0 Yuling Gu, et al. ∙

research

∙ 10/21/2022

Entailer: Answering Questions with Faithful and Truthful Chains of Reasoning

Our goal is a question-answering (QA) system that can show how its answe...

0 Oyvind Tafjord, et al. ∙

research

∙ 10/05/2022

Decomposed Prompting: A Modular Approach for Solving Complex Tasks

Few-shot prompting is a surprisingly powerful way to use Large Language ...

2 Tushar Khot, et al. ∙

research

∙ 10/03/2022

Complexity-Based Prompting for Multi-Step Reasoning

We study the task of prompting large-scale language models to perform mu...

4 Yao Fu, et al. ∙

research

∙ 09/20/2022

Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering

When answering a question, humans utilize the information available acro...

73 Pan Lu @ UCLA, et al. ∙

research

∙ 04/27/2022

Towards Teachable Reasoning Systems

Our goal is a teachable reasoning system for question-answering (QA), wh...

0 Bhavana Dalvi, et al. ∙

research

∙ 04/19/2022

What Makes Instruction Learning Hard? An Investigation and a New Challenge in a Synthetic Environment

The instruction learning paradigm – where a model learns to perform new ...

11 Matthew Finlayson, et al. ∙

research

∙ 04/12/2022

NumGLUE: A Suite of Fundamental yet Challenging Mathematical Reasoning Tasks

Given the ubiquitous nature of numbers in text, reasoning with numbers t...

6 Swaroop Mishra, et al. ∙

research

∙ 01/16/2022

Memory-assisted prompt editing to improve GPT-3 after deployment

Large LMs such as GPT-3, while powerful, are not immune to mistakes, but...

0 Aman Madaan, et al. ∙

research

∙ 12/16/2021

Improving scripts with a memory of natural feedback

How can an end-user provide feedback if a deployed structured prediction...

0 Niket Tandon, et al. ∙

research

∙ 12/16/2021

DREAM: Uncovering Mental Models behind Language Models

To what extent do language models (LMs) build "mental models" of a scene...

0 Yuling Gu, et al. ∙

research

∙ 12/15/2021

Interscript: A dataset for interactive learning of scripts through error feedback

How can an end-user provide feedback if a deployed structured prediction...

0 Niket Tandon, et al. ∙

research

∙ 10/27/2021

How Much Coffee Was Consumed During EMNLP 2019? Fermi Problems: A New Reasoning Challenge for AI

Many real-world problems require the combined application of multiple re...

5 Ashwin Kalyan, et al. ∙

research

∙ 09/29/2021

BeliefBank: Adding Memory to a Pre-Trained Language Model for a Systematic Notion of Belief

Although pretrained language models (PTLMs) contain significant amounts ...

0 Nora Kassner, et al. ∙

research

∙ 09/06/2021

General-Purpose Question-Answering with Macaw

Despite the successes of pretrained language models, there are still few...

0 Oyvind Tafjord, et al. ∙

research

∙ 04/18/2021

Improving Neural Model Performance through Natural Language Feedback on Their Explanations

A class of explainable NLP models for reasoning tasks support their deci...

0 Aman Madaan, et al. ∙

research

∙ 04/17/2021

Explaining Answers with Entailment Trees

Our goal, in the context of open-domain textual question-answering (QA),...

0 Bhavana Dalvi, et al. ∙

research

∙ 04/16/2021

Enriching a Model's Notion of Belief using a Persistent Memory

Although pretrained language models (PTLMs) have been shown to contain s...

0 Nora Kassner, et al. ∙

research

∙ 04/16/2021

proScript: Partially Ordered Scripts Generation via Pre-trained Language Models

Scripts - standardized event sequences describing typical everyday activ...

0 Keisuke Sakaguchi, et al. ∙

research

∙ 02/05/2021

Think you have Solved Direct-Answer Question Answering? Try ARC-DA, the Direct-Answer AI2 Reasoning Challenge

We present the ARC-DA dataset, a direct-answer ("open response", "freefo...

0 Sumithra Bhakthavatsalam, et al. ∙

research

∙ 12/24/2020

ProofWriter: Generating Implications, Proofs, and Abductive Statements over Natural Language

Transformers have been shown to emulate logical deduction over natural l...

7 Oyvind Tafjord, et al. ∙

research

∙ 10/07/2020

Learning to Explain: Datasets and Models for Identifying Valid Reasoning Chains in Multihop Question-Answering

Despite the rapid progress in multihop question-answering (QA), models s...

0 Harsh Jhamtani, et al. ∙

research

∙ 09/01/2020

Text Modular Networks: Learning to Decompose Tasks in the Language of Existing Models

A common approach to solve complex tasks is by breaking them down into s...

7 Tushar Khot, et al. ∙

research

∙ 06/12/2020

Do Dogs have Whiskers? A New Knowledge Base of hasPart Relations

We present a new knowledge-base of hasPart relationships, extracted from...

0 Sumithra Bhakthavatsalam, et al. ∙

research

∙ 06/11/2020

Teaching Pre-Trained Models to Systematically Reason Over Implicit Knowledge

To what extent can a neural network systematically reason over symbolic ...

0 Alon Talmor, et al. ∙

research

∙ 05/08/2020

Knowledge Patterns

This paper describes a new technique, called "knowledge patterns", for h...

19 Peter Clark, et al. ∙

research

∙ 05/02/2020

UnifiedQA: Crossing Format Boundaries With a Single QA System

Question answering (QA) tasks have been posed using a variety of formats...

4 Daniel Khashabi, et al. ∙

research

∙ 05/02/2020

GenericsKB: A Knowledge Base of Generic Statements

We present a new resource for the NLP community, namely a large (3.5M+ s...

0 Sumithra Bhakthavatsalam, et al. ∙

research

∙ 02/14/2020

Transformers as Soft Reasoners over Language

AI has long pursued the goal of having systems reason over *explicitly p...

0 Peter Clark, et al. ∙

research

∙ 10/25/2019

QASC: A Dataset for Question Answering via Sentence Composition

Composing knowledge from multiple pieces of texts is a key challenge in ...

0 Tushar Khot, et al. ∙

research

∙ 09/19/2019

What's Missing: A Knowledge Gap Guided Approach for Multi-hop Question Answering

Multi-hop textual question answering requires combining information from...

0 Tushar Khot, et al. ∙

research

∙ 09/10/2019

Everything Happens for a Reason: Discovering the Purpose of Actions in Procedural Text

Our goal is to better comprehend procedural text, e.g., a paragraph abou...

0 Bhavana Dalvi Mishra, et al. ∙

research

∙ 09/10/2019

WIQA: A dataset for "What if..." reasoning over procedural text

We introduce WIQA, the first large-scale dataset of "What if..." questio...

0 Niket Tandon, et al. ∙

research

∙ 09/08/2019

QuaRTz: An Open-Domain Dataset of Qualitative Relationship Questions

We introduce the first open-domain dataset, called QuaRTz, for reasoning...

0 Oyvind Tafjord, et al. ∙

research

∙ 09/04/2019

From 'F' to 'A' on the N.Y. Regents Science Exams: An Overview of the Aristo Project

AI has achieved remarkable mastery over games such as Chess, Go, and Pok...

8 Peter Clark, et al. ∙

research

∙ 08/16/2019

Reasoning Over Paragraph Effects in Situations

A key component of successfully reading a passage of text is the ability...

0 Kevin Lin, et al. ∙

research

∙ 08/15/2019

Multi-class Hierarchical Question Classification for Multiple Choice Science Exams

Prior work has demonstrated that question classification (QC), recognizi...

0 Dongfang Xu, et al. ∙

research

∙ 06/21/2019

Be Consistent! Improving Procedural Text Comprehension using Label Consistency

Our goal is procedural text comprehension, namely tracking how the prope...

0 Xinya Du, et al. ∙

research

∙ 05/01/2019

Declarative Question Answering over Knowledge Bases containing Natural Language Text with Answer Set Programming

While in recent years machine learning (ML) based approaches have been t...

0 Arindam Mitra, et al. ∙

research

∙ 11/20/2018

QuaRel: A Dataset and Models for Answering Questions about Qualitative Relationships

Many natural language questions require recognizing and reasoning with q...

0 Oyvind Tafjord, et al. ∙

research

∙ 09/08/2018

Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering

We present a new kind of question answering dataset, OpenBookQA, modeled...

0 Todor Mihaylov, et al. ∙

research

∙ 08/29/2018

Reasoning about Actions and State Changes by Injecting Commonsense Knowledge

Comprehending procedural text, e.g., a paragraph describing photosynthes...

0 Niket Tandon, et al. ∙

Peter Clark

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