1 citations · 2 across the 8 of their papers we have counts for
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Multi-Functional Embedding Models for Funder Name Disambiguation in Scientific Publication Records
Kanyao Han, Zhiwen You, Jinseok Kim +1
Understanding the historical allocation and distribution of research funding advances our knowledge of how scientific research is supported across fields, institutions, and regions…
CoBia: Constructed Conversations Can Trigger Otherwise Concealed Societal Biases in LLMs
Nafiseh Nikeghbal, Amir Hossein Kargaran, Jana Diesner
Improvements in model construction, including fortified safety guardrails, allow Large language models (LLMs) to increasingly pass standard safety checks. However, LLMs sometimes s…
Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes
Sullam Jeoung, Yubin Ge, Haohan Wang +1
Examining the alignment of large language models (LLMs) has become increasingly important, e.g., when LLMs fail to operate as intended. This study examines the alignment of LLMs wi…
SciPrompt: Knowledge-augmented Prompting for Fine-grained Categorization of Scientific Topics
Zhiwen You, Kanyao Han, Haotian Zhu +2
Prompt-based fine-tuning has become an essential method for eliciting information encoded in pre-trained language models for a variety of tasks, including text classification. For…
MEXA: Multilingual Evaluation of English-Centric LLMs via Cross-Lingual Alignment
Amir Hossein Kargaran, Ali Modarressi, Nafiseh Nikeghbal +3
English-centric large language models (LLMs) often show strong multilingual capabilities. However, their multilingual performance remains unclear and is under-evaluated for many ot…
Beyond Binary Gender Labels: Revealing Gender Biases in LLMs through Gender-Neutral Name Predictions
Zhiwen You, HaeJin Lee, Shubhanshu Mishra +4
Name-based gender prediction has traditionally categorized individuals as either female or male based on their names, using a binary classification system. That binary approach can…
StereoMap: Quantifying the Awareness of Human-like Stereotypes in Large Language Models
Sullam Jeoung, Yubin Ge, Jana Diesner
Large Language Models (LLMs) have been observed to encode and perpetuate harmful associations present in the training data. We propose a theoretically grounded framework called Ste…