Search Results for author: Shehzaad Dhuliawala

Found 18 papers, 10 papers with code

A Diachronic Perspective on User Trust in AI under Uncertainty

1 code implementation20 Oct 2023 Shehzaad Dhuliawala, Vilém Zouhar, Mennatallah El-Assady, Mrinmaya Sachan

In a human-AI collaboration, users build a mental model of the AI system based on its reliability and how it presents its decision, e. g. its presentation of system confidence and an explanation of the output.

Chain-of-Verification Reduces Hallucination in Large Language Models

1 code implementation20 Sep 2023 Shehzaad Dhuliawala, Mojtaba Komeili, Jing Xu, Roberta Raileanu, Xian Li, Asli Celikyilmaz, Jason Weston

Generation of plausible yet incorrect factual information, termed hallucination, is an unsolved issue in large language models.

Hallucination Text Generation

Variational Classification

1 code implementation17 May 2023 Shehzaad Dhuliawala, Mrinmaya Sachan, Carl Allen

We present a latent variable model for classification that provides a novel probabilistic interpretation of neural network softmax classifiers.

Adversarial Robustness text-classification +1

Extracting Victim Counts from Text

1 code implementation23 Feb 2023 Mian Zhong, Shehzaad Dhuliawala, Niklas Stoehr

We cast victim count extraction as a question answering (QA) task with a regression or classification objective.

Dependency Parsing Humanitarian +2

Calibration of Machine Reading Systems at Scale

no code implementations Findings (ACL) 2022 Shehzaad Dhuliawala, Leonard Adolphs, Rajarshi Das, Mrinmaya Sachan

We show that calibrating such complex systems which contain discrete retrieval and deep reading components is challenging and current calibration techniques fail to scale to these settings.

Claim Verification Open-Domain Question Answering +2

Case-based Reasoning for Better Generalization in Textual Reinforcement Learning

no code implementations ICLR 2022 Mattia Atzeni, Shehzaad Dhuliawala, Keerthiram Murugesan, Mrinmaya Sachan

Text-based games (TBG) have emerged as promising environments for driving research in grounded language understanding and studying problems like generalization and sample efficiency.

Out-of-Distribution Generalization reinforcement-learning +2

How to Query Language Models?

1 code implementation4 Aug 2021 Leonard Adolphs, Shehzaad Dhuliawala, Thomas Hofmann

We apply this approach of querying by example to the LAMA probe and obtain substantial improvements of up to 37. 8% for BERT-large on the T-REx data when providing only 10 demonstrations--even outperforming a baseline that queries the model with up to 40 paraphrases of the question.

A Simple Approach to Case-Based Reasoning in Knowledge Bases

1 code implementation AKBC 2020 Rajarshi Das, Ameya Godbole, Shehzaad Dhuliawala, Manzil Zaheer, Andrew McCallum

We present a surprisingly simple yet accurate approach to reasoning in knowledge graphs (KGs) that requires \emph{no training}, and is reminiscent of case-based reasoning in classical artificial intelligence (AI).

Knowledge Graphs Meta-Learning +1

Towards Domain Adaptation from Limited Data for Question Answering Using Deep Neural Networks

no code implementations6 Nov 2019 Timothy J. Hazen, Shehzaad Dhuliawala, Daniel Boies

This paper explores domain adaptation for enabling question answering (QA) systems to answer questions posed against documents in new specialized domains.

Domain Adaptation Question Answering +1

Chains-of-Reasoning at TextGraphs 2019 Shared Task: Reasoning over Chains of Facts for Explainable Multi-hop Inference

no code implementations WS 2019 Rajarshi Das, Ameya Godbole, Manzil Zaheer, Shehzaad Dhuliawala, Andrew McCallum

This paper describes our submission to the shared task on {``}Multi-hop Inference Explanation Regeneration{''} in TextGraphs workshop at EMNLP 2019 (Jansen and Ustalov, 2019).

Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning

7 code implementations ICLR 2018 Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Luke Vilnis, Ishan Durugkar, Akshay Krishnamurthy, Alex Smola, Andrew McCallum

Knowledge bases (KB), both automatically and manually constructed, are often incomplete --- many valid facts can be inferred from the KB by synthesizing existing information.

Navigate Relation +1

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