Search Results for author:
Found 27 papers, 10 papers with code
One Document, Many Revisions: A Dataset for Classification and Description of Edit Intents
Document authoring involves a lengthy revision process, marked by individual edits that are frequently linked to comments.
Think about it! Improving defeasible reasoning by first modeling the question scenario.
Defeasible reasoning is the mode of reasoning where conclusions can be overturned by taking into account new evidence.
Confidence Calibration and Rationalization for LLMs via Multi-Agent Deliberation
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).
How Far Can We Extract Diverse Perspectives from Large Language Models?
In this study, we investigate LLMs' capacity for generating diverse perspectives and rationales on subjective topics, such as social norms and argumentative texts.
AutoMix: Automatically Mixing Language Models
Large language models (LLMs) are now available from cloud API providers in various sizes and configurations.
StyLEx: Explaining Style Using Human Lexical Annotations
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).
Conditional set generation using Seq2seq models
Conditional set generation learns a mapping from an input sequence of tokens to a set.
Counterfactual Data Augmentation improves Factuality of Abstractive Summarization
Abstractive summarization systems based on pretrained language models often generate coherent but factually inconsistent sentences.
Template Filling for Controllable Commonsense Reasoning
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.
Think about it! Improving defeasible reasoning by first modeling the question scenario
Defeasible reasoning is the mode of reasoning where conclusions can be overturned by taking into account new evidence.
Could you give me a hint? Generating inference graphs for defeasible reasoning
Defeasible reasoning is the mode of reasoning where conclusions can be overturned by taking into account new evidence.
Improving Neural Model Performance through Natural Language Feedback on Their Explanations
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?
CURIE: An Iterative Querying Approach for Reasoning About Situations
Recently, models have been shown to predict the effects of unexpected situations, e. g., would cloudy skies help or hinder plant growth?
SelfExplain: A Self-Explaining Architecture for Neural Text Classifiers
We introduce SelfExplain, a novel self-explaining model that explains a text classifier's predictions using phrase-based concepts.
A Dataset for Tracking Entities in Open Domain Procedural Text
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.
EIGEN: Event Influence GENeration using Pre-trained Language Models
Reasoning about events and tracking their influences is fundamental to understanding processes.
What-if I ask you to explain: Explaining the effects of perturbations in procedural text
We address the task of explaining the effects of perturbations in procedural text, an important test of process comprehension.
StructSum: Summarization via Structured Representations
To this end, we propose incorporating latent and explicit dependencies across sentences in the source document into end-to-end single-document summarization models.
Modeling the Relationship between User Comments and Edits in Document Revision
Thus, in this paper we explore the relationship between comments and edits by defining two novel, related tasks: Comment Ranking and Edit Anchoring.
Domain Adaptation of SRL Systems for Biological Processes
Domain adaptation remains one of the most challenging aspects in the wide-spread use of Semantic Role Labeling (SRL) systems.
Learning to Define Terms in the Software Domain
One way to test a person{'}s knowledge of a domain is to ask them to define domain-specific terms.
Simple and Effective Semi-Supervised Question Answering
Recent success of deep learning models for the task of extractive Question Answering (QA) is hinged on the availability of large annotated corpora.
Gated-Attention Architectures for Task-Oriented Language Grounding
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.
Generating Questions and Multiple-Choice Answers using Semantic Analysis of Texts
We present a novel approach to automated question generation that improves upon prior work both from a technology perspective and from an assessment perspective.
