9 citations · 23 across the 7 of their papers we have counts for
9 papers
Revisiting LLM Value Probing Strategies: Are They Robust and Expressive?
Siqi Shen, Mehar Singh, Lajanugen Logeswaran +3
There has been extensive research on assessing the value orientation of Large Language Models (LLMs) as it can shape user experiences across demographic groups. However, several ch…
Understanding the Capabilities and Limitations of Large Language Models for Cultural Commonsense
Siqi Shen, Lajanugen Logeswaran, Moontae Lee +3
Large language models (LLMs) have demonstrated substantial commonsense understanding through numerous benchmark evaluations. However, their understanding of cultural commonsense re…
The Generation Gap: Exploring Age Bias in the Value Systems of Large Language Models
Siyang Liu, Trish Maturi, Bowen Yi +2
We explore the alignment of values in Large Language Models (LLMs) with specific age groups, leveraging data from the World Value Survey across thirteen categories. Through a diver…
Has It All Been Solved? Open NLP Research Questions Not Solved by Large Language Models
Oana Ignat, Zhijing Jin, Artem Abzaliev +19
Recent progress in large language models (LLMs) has enabled the deployment of many generative NLP applications. At the same time, it has also led to a misleading public discourse t…
Multiview Contextual Commonsense Inference: A New Dataset and Task
Siqi Shen, Deepanway Ghosal, Navonil Majumder +3
Contextual commonsense inference is the task of generating various types of explanations around the events in a dyadic dialogue, including cause, motivation, emotional reaction, an…
CICERO: A Dataset for Contextualized Commonsense Inference in Dialogues
Deepanway Ghosal, Siqi Shen, Navonil Majumder +2
This paper addresses the problem of dialogue reasoning with contextualized commonsense inference. We curate CICERO, a dataset of dyadic conversations with five types of utterance-l…
CIDER: Commonsense Inference for Dialogue Explanation and Reasoning
Deepanway Ghosal, Pengfei Hong, Siqi Shen +3
Commonsense inference to understand and explain human language is a fundamental research problem in natural language processing. Explaining human conversations poses a great challe…
Expressive Interviewing: A Conversational System for Coping with COVID-19
Charles Welch, Allison Lahnala, Verónica Pérez-Rosas +6
The ongoing COVID-19 pandemic has raised concerns for many regarding personal and public health implications, financial security and economic stability. Alongside many other unprec…
Fixed-size Objects Encoding for Visual Relationship Detection
Hengyue Pan, Xin Niu, Rongchun Li +2
In this paper, we propose a fixed-size object encoding method (FOE-VRD) to improve performance of visual relationship detection tasks. Comparing with previous methods, FOE-VRD has…