1 code implementation • EMNLP 2021 • Aman Madaan, Niket Tandon, Dheeraj Rajagopal, Peter Clark, Yiming Yang, Eduard Hovy
Defeasible reasoning is the mode of reasoning where conclusions can be overturned by taking into account new evidence.
no code implementations • LREC 2022 • Dheeraj Rajagopal, Xuchao Zhang, Michael Gamon, Sujay Kumar Jauhar, Diyi Yang, Eduard Hovy
Document authoring involves a lengthy revision process, marked by individual edits that are frequently linked to comments.
1 code implementation • 14 Apr 2024 • Ruixin Yang, Dheeraj Rajagopal, Shirley Anugrah Hayati, Bin Hu, Dongyeop Kang
Uncertainty estimation is a significant issue for current large language models (LLMs) that are generally poorly calibrated and over-confident, especially with reinforcement learning from human feedback (RLHF).
1 code implementation • 16 Nov 2023 • Shirley Anugrah Hayati, Minhwa Lee, Dheeraj Rajagopal, Dongyeop Kang
In this study, we investigate LLMs' capacity for generating diverse perspectives and rationales on subjective topics, such as social norms and argumentative texts.
1 code implementation • 19 Oct 2023 • Aman Madaan, Pranjal Aggarwal, Ankit Anand, Srividya Pranavi Potharaju, Swaroop Mishra, Pei Zhou, Aditya Gupta, Dheeraj Rajagopal, Karthik Kappaganthu, Yiming Yang, Shyam Upadhyay, Mausam, Manaal Faruqui
Large language models (LLMs) are now available from cloud API providers in various sizes and configurations.
1 code implementation • 14 Oct 2022 • Shirley Anugrah Hayati, Kyumin Park, Dheeraj Rajagopal, Lyle Ungar, Dongyeop Kang
Large pre-trained language models have achieved impressive results on various style classification tasks, but they often learn spurious domain-specific words to make predictions (Hayati et al., 2021).
no code implementations • 25 May 2022 • Dheeraj Rajagopal, Siamak Shakeri, Cicero Nogueira dos santos, Eduard Hovy, Chung-Ching Chang
Abstractive summarization systems based on pretrained language models often generate coherent but factually inconsistent sentences.
no code implementations • 25 May 2022 • Aman Madaan, Dheeraj Rajagopal, Niket Tandon, Yiming Yang, Antoine Bosselut
Conditional set generation learns a mapping from an input sequence of tokens to a set.
no code implementations • 31 Oct 2021 • Dheeraj Rajagopal, Vivek Khetan, Bogdan Sacaleanu, Anatole Gershman, Andrew Fano, Eduard Hovy
To enable better controllability, we propose to study the commonsense reasoning as a template filling task (TemplateCSR) -- where the language models fills reasoning templates with the given constraints as control factors.
1 code implementation • 24 Oct 2021 • Aman Madaan, Niket Tandon, Dheeraj Rajagopal, Peter Clark, Yiming Yang, Eduard Hovy
Defeasible reasoning is the mode of reasoning where conclusions can be overturned by taking into account new evidence.
1 code implementation • AKBC Workshop CSKB 2021 • Aman Madaan, Dheeraj Rajagopal, Niket Tandon, Yiming Yang, Eduard Hovy
Defeasible reasoning is the mode of reasoning where conclusions can be overturned by taking into account new evidence.
no code implementations • 18 Apr 2021 • Aman Madaan, Niket Tandon, Dheeraj Rajagopal, Yiming Yang, Peter Clark, Keisuke Sakaguchi, Ed Hovy
A class of explainable NLP models for reasoning tasks support their decisions by generating free-form or structured explanations, but what happens when these supporting structures contain errors?
1 code implementation • CSRR (ACL) 2022 • Dheeraj Rajagopal, Aman Madaan, Niket Tandon, Yiming Yang, Shrimai Prabhumoye, Abhilasha Ravichander, Peter Clark, Eduard Hovy
Recently, models have been shown to predict the effects of unexpected situations, e. g., would cloudy skies help or hinder plant growth?
2 code implementations • EMNLP 2021 • Dheeraj Rajagopal, Vidhisha Balachandran, Eduard Hovy, Yulia Tsvetkov
We introduce SelfExplain, a novel self-explaining model that explains a text classifier's predictions using phrase-based concepts.
no code implementations • EMNLP 2020 • Niket Tandon, Keisuke Sakaguchi, Bhavana Dalvi Mishra, Dheeraj Rajagopal, Peter Clark, Michal Guerquin, Kyle Richardson, Eduard Hovy
Our solution is a new task formulation where given just a procedural text as input, the task is to generate a set of state change tuples(entity, at-tribute, before-state, after-state)for each step, where the entity, attribute, and state values must be predicted from an open vocabulary.
no code implementations • 22 Oct 2020 • Aman Madaan, Dheeraj Rajagopal, Yiming Yang, Abhilasha Ravichander, Eduard Hovy, Shrimai Prabhumoye
Reasoning about events and tracking their influences is fundamental to understanding processes.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Dheeraj Rajagopal, Niket Tandon, Bhavana Dalvi, Peter Clark, Eduard Hovy
We address the task of explaining the effects of perturbations in procedural text, an important test of process comprehension.
1 code implementation • EACL 2021 • Vidhisha Balachandran, Artidoro Pagnoni, Jay Yoon Lee, Dheeraj Rajagopal, Jaime Carbonell, Yulia Tsvetkov
To this end, we propose incorporating latent and explicit dependencies across sentences in the source document into end-to-end single-document summarization models.
no code implementations • IJCNLP 2019 • Xuchao Zhang, Dheeraj Rajagopal, Michael Gamon, Sujay Kumar Jauhar, Chang-Tien Lu
Thus, in this paper we explore the relationship between comments and edits by defining two novel, related tasks: Comment Ranking and Edit Anchoring.
no code implementations • WS 2019 • Dheeraj Rajagopal, Nidhi Vyas, Aditya Siddhant, Anirudha Rayasam, T, Niket on, Eduard Hovy
Domain adaptation remains one of the most challenging aspects in the wide-spread use of Semantic Role Labeling (SRL) systems.
no code implementations • WS 2018 • Balach, Vidhisha ran, Dheeraj Rajagopal, Rose Catherine Kanjirathinkal, William Cohen
One way to test a person{'}s knowledge of a domain is to ask them to define domain-specific terms.
no code implementations • NAACL 2018 • Bhuwan Dhingra, Danish Pruthi, Dheeraj Rajagopal
Recent success of deep learning models for the task of extractive Question Answering (QA) is hinged on the availability of large annotated corpora.
1 code implementation • 22 Jun 2017 • Devendra Singh Chaplot, Kanthashree Mysore Sathyendra, Rama Kumar Pasumarthi, Dheeraj Rajagopal, Ruslan Salakhutdinov
To perform tasks specified by natural language instructions, autonomous agents need to extract semantically meaningful representations of language and map it to visual elements and actions in the environment.
no code implementations • COLING 2016 • Jun Araki, Dheeraj Rajagopal, Sreecharan Sankaranarayanan, Susan Holm, Yukari Yamakawa, Teruko Mitamura
We present a novel approach to automated question generation that improves upon prior work both from a technology perspective and from an assessment perspective.