7 citations · 13 across the 5 of their papers we have counts for
6 papers · 1 filter
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…
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…