Ashish Sabharwal

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

∙ 05/23/2023

Improving Language Models via Plug-and-Play Retrieval Feedback

Large language models (LLMs) exhibit remarkable performance across vario...

0 Wenhao Yu, et al. ∙

research

∙ 01/30/2023

Specializing Smaller Language Models towards Multi-Step Reasoning

The surprising ability of Large Language Models (LLMs) to perform well o...

0 Yao Fu, et al. ∙

research

∙ 12/20/2022

DISCO: Distilling Phrasal Counterfactuals with Large Language Models

Recent methods demonstrate that data augmentation using counterfactual k...

0 Zeming Chen, et al. ∙

research

∙ 12/20/2022

Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions

Recent work has shown that large language models are capable of generati...

0 Harsh Trivedi, et al. ∙

research

∙ 11/15/2022

Breakpoint Transformers for Modeling and Tracking Intermediate Beliefs

Can we teach natural language understanding models to track their belief...

0 Kyle Richardson, 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

∙ 10/06/2022

Transformers Can Be Expressed In First-Order Logic with Majority

Characterizing the implicit structure of the computation within neural n...

0 William Merrill, 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

∙ 07/02/2022

Log-Precision Transformers are Constant-Depth Uniform Threshold Circuits

We prove that transformer neural networks with logarithmic precision in ...

0 William Merrill, et al. ∙

research

∙ 05/25/2022

Teaching Broad Reasoning Skills via Decomposition-Guided Contexts

Question-answering datasets require a broad set of reasoning skills. We ...

0 Harsh Trivedi, et al. ∙

research

∙ 05/07/2022

Better Retrieval May Not Lead to Better Question Answering

Considerable progress has been made recently in open-domain question ans...

0 Zhengzhong Liang, 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

∙ 12/16/2021

Pushing the Limits of Rule Reasoning in Transformers through Natural Language Satisfiability

Investigating the reasoning abilities of transformer models, and discove...

0 Kyle Richardson, 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

∙ 10/16/2021

Learning to Solve Complex Tasks by Talking to Agents

Humans often solve complex problems by interacting (in natural language)...

0 Tushar Khot, et al. ∙

research

∙ 08/02/2021

MuSiQue: Multi-hop Questions via Single-hop Question Composition

To build challenging multi-hop question answering datasets, we propose a...

0 Harsh Trivedi, et al. ∙

research

∙ 06/02/2021

Ethical-Advice Taker: Do Language Models Understand Natural Language Interventions?

Is it possible to use natural language to intervene in a model's behavio...

10 Jieyu Zhao, et al. ∙

research

∙ 04/18/2021

GooAQ: Open Question Answering with Diverse Answer Types

While day-to-day questions come with a variety of answer types, the curr...

0 Daniel Khashabi, et al. ∙

research

∙ 03/23/2021

Multi-Modal Answer Validation for Knowledge-Based VQA

The problem of knowledge-based visual question answering involves answer...

2 Jialin Wu, 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

∙ 10/24/2020

ReadOnce Transformers: Reusable Representations of Text for Transformers

While large-scale language models are extremely effective when directly ...

0 Shih-Ting Lin, et al. ∙

research

∙ 10/24/2020

Temporal Reasoning on Implicit Events from Distant Supervision

Existing works on temporal reasoning among events described in text focu...

0 Ben Zhou, et al. ∙

research

∙ 10/06/2020

UNQOVERing Stereotyping Biases via Underspecified Questions

While language embeddings have been shown to have stereotyping biases, h...

0 Tao Li, 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

∙ 07/01/2020

Belief Propagation Neural Networks

Learned neural solvers have successfully been used to solve combinatoria...

14 Jonathan Kuck, et al. ∙

research

∙ 05/02/2020

Measuring and Reducing Non-Multifact Reasoning in Multi-hop Question Answering

The measurement of true progress in multihop question-answering has been...

0 Harsh Trivedi, 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

∙ 04/14/2020

A Simple Yet Strong Pipeline for HotpotQA

State-of-the-art models for multi-hop question answering typically augme...

0 Dirk Groeneveld, et al. ∙

research

∙ 04/09/2020

Natural Perturbation for Robust Question Answering

While recent models have achieved human-level scores on many NLP dataset...

1 Daniel Khashabi, et al. ∙

research

∙ 02/10/2020

Adversarial Filters of Dataset Biases

Large neural models have demonstrated human-level performance on languag...

14 Ronan Le Bras, et al. ∙

research

∙ 12/31/2019

What Does My QA Model Know? Devising Controlled Probes using Expert Knowledge

Open-domain question answering (QA) is known to involve several underlyi...

0 Kyle Richardson, et al. ∙

research

∙ 11/26/2019

Approximating the Permanent by Sampling from Adaptive Partitions

Computing the permanent of a non-negative matrix is a core problem with ...

12 Jonathan Kuck, et al. ∙

research

∙ 11/10/2019

Not All Claims are Created Equal: Choosing the Right Approach to Assess Your Hypotheses

Empirical research in Natural Language Processing (NLP) has adopted a na...

8 Erfan Sadeqi Azer, 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

∙ 10/13/2019

AdaWISH: Faster Discrete Integration via Adaptive Quantiles

Discrete integration in a high dimensional space of n variables poses fu...

15 Fan Ding, 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

∙ 09/16/2019

Probing Natural Language Inference Models through Semantic Fragments

Do state-of-the-art models for language understanding already have, or c...

0 Kyle Richardson, 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

∙ 06/09/2019

Question Answering as Global Reasoning over Semantic Abstractions

We propose a novel method for exploiting the semantic structure of text ...

3 Daniel Khashabi, et al. ∙

research

∙ 04/20/2019

Repurposing Entailment for Multi-Hop Question Answering Tasks

Question Answering (QA) naturally reduces to an entailment problem, name...

0 Harsh Trivedi, et al. ∙

research

∙ 01/08/2019

On the Capabilities and Limitations of Reasoning for Natural Language Understanding

Recent systems for natural language understanding are strong at overcomi...

12 Daniel Khashabi, 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

∙ 11/02/2018

Exploiting Explicit Paths for Multi-hop Reading Comprehension

We focus on the task of multi-hop reading comprehension where a system i...

0 Souvik Kundu, 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

∙ 08/28/2018

Bridging Knowledge Gaps in Neural Entailment via Symbolic Models

Most textual entailment models focus on lexical gaps between the premise...

0 Dongyeop Kang, et al. ∙

research

∙ 05/12/2018

AdvEntuRe: Adversarial Training for Textual Entailment with Knowledge-Guided Examples

We consider the problem of learning textual entailment models with limit...

4 Dongyeop Kang, et al. ∙

Ashish Sabharwal

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