Search Results for author:
Found 25 papers, 14 papers with code
Mitigating Hallucination in Abstractive Summarization with Domain-Conditional Mutual Information
We hypothesize that the domain (or topic) of the source text triggers the model to generate text that is highly probable in the domain, neglecting the details of the source text.
Ever-Evolving Memory by Blending and Refining the Past
To effectively construct memory, it is crucial to seamlessly connect past and present information, while also possessing the ability to forget obstructive information.
Ask Optimal Questions: Aligning Large Language Models with Retriever's Preference in Conversational Search
Furthermore, we fine-tune a smaller LM using this dataset to align it with the retrievers' preferences as feedback.
Generalizing Visual Question Answering from Synthetic to Human-Written Questions via a Chain of QA with a Large Language Model
Visual question answering (VQA) is a task where an image is given, and a series of questions are asked about the image.
Multi-User MultiWOZ: Task-Oriented Dialogues among Multiple Users
While most task-oriented dialogues assume conversations between the agent and one user at a time, dialogue systems are increasingly expected to communicate with multiple users simultaneously who make decisions collaboratively.
From Values to Opinions: Predicting Human Behaviors and Stances Using Value-Injected Large Language Models
Being able to predict people's opinions on issues and behaviors in realistic scenarios can be helpful in various domains, such as politics and marketing.
KG-GPT: A General Framework for Reasoning on Knowledge Graphs Using Large Language Models
While large language models (LLMs) have made considerable advancements in understanding and generating unstructured text, their application in structured data remains underexplored.
Publicly Shareable Clinical Large Language Model Built on Synthetic Clinical Notes
The development of large language models tailored for handling patients' clinical notes is often hindered by the limited accessibility and usability of these notes due to strict privacy regulations.
Open-WikiTable: Dataset for Open Domain Question Answering with Complex Reasoning over Table
Despite recent interest in open domain question answering (ODQA) over tables, many studies still rely on datasets that are not truly optimal for the task with respect to utilizing structural nature of table.
FactKG: Fact Verification via Reasoning on Knowledge Graphs
KGs can be a valuable knowledge source in fact verification due to their reliability and broad applicability.
A Closer Look at the Intervention Procedure of Concept Bottleneck Models
Concept bottleneck models (CBMs) are a class of interpretable neural network models that predict the target response of a given input based on its high-level concepts.
Knowledge-Enhanced Evidence Retrieval for Counterargument Generation
Finding counterevidence to statements is key to many tasks, including counterargument generation.
Classifying Argumentative Relations Using Logical Mechanisms and Argumentation Schemes
While argument mining has achieved significant success in classifying argumentative relations between statements (support, attack, and neutral), we have a limited computational understanding of logical mechanisms that constitute those relations.
Extracting Implicitly Asserted Propositions in Argumentation
Our study may inform future research on argument mining and the semantics of these rhetorical devices in argumentation.
Detecting Attackable Sentences in Arguments
Finding attackable sentences in an argument is the first step toward successful refutation in argumentation.
Machine-Aided Annotation for Fine-Grained Proposition Types in Argumentation
We introduce a corpus of the 2016 U. S. presidential debates and commentary, containing 4, 648 argumentative propositions annotated with fine-grained proposition types.
A Cascade Model for Proposition Extraction in Argumentation
Propositions are the basic units of an argument and the primary building blocks of most argument mining systems.
Using Functional Schemas to Understand Social Media Narratives
We propose a novel take on understanding narratives in social media, focusing on learning {''}functional story schemas{''}, which consist of sets of stereotypical functional structures.
Attentive Interaction Model: Modeling Changes in View in Argumentation
We present a neural architecture for modeling argumentative dialogue that explicitly models the interplay between an Opinion Holder's (OH's) reasoning and a challenger's argument, with the goal of predicting if the argument successfully changes the OH's view.
Roles and Success in Wikipedia Talk Pages: Identifying Latent Patterns of Behavior
In this work we investigate how role-based behavior profiles of a Wikipedia editor, considered against the backdrop of roles taken up by other editors in discussions, predict the success of the editor at achieving an impact on the associated article.
Combining LSTM and Latent Topic Modeling for Mortality Prediction
There is a great need for technologies that can predict the mortality of patients in intensive care units with both high accuracy and accountability.
Modeling Dialogue Acts with Content Word Filtering and Speaker Preferences
We present an unsupervised model of dialogue act sequences in conversation.
Simplifying the Bible and Wikipedia Using Statistical Machine Translation
I started this work with the hope of generating a text synthesizer (like a musical synthesizer) that can imitate certain linguistic styles.
