4 citations · 12 across the 10 of their papers we have counts for
6 papers · 1 filter
Task-Agnostic Low-Rank Adapters for Unseen English Dialects
Zedian Xiao, William Held, Yanchen Liu +1
Large Language Models (LLMs) are trained on corpora disproportionally weighted in favor of Standard American English. As a result, speakers of other dialects experience significant…
Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach
Yanchen Liu, Mingyu Derek Ma, Wenna Qin +5
Susceptibility to misinformation describes the degree of belief in unverifiable claims, a latent aspect of individuals' mental processes that is not observable. Existing susceptibi…
Confronting LLMs with Traditional ML: Rethinking the Fairness of Large Language Models in Tabular Classifications
Yanchen Liu, Srishti Gautam, Jiaqi Ma +1
Recent literature has suggested the potential of using large language models (LLMs) to make classifications for tabular tasks. However, LLMs have been shown to exhibit harmful soci…
MIDDAG: Where Does Our News Go? Investigating Information Diffusion via Community-Level Information Pathways
Mingyu Derek Ma, Alexander K. Taylor, Nuan Wen +9
We present MIDDAG, an intuitive, interactive system that visualizes the information propagation paths on social media triggered by COVID-19-related news articles accompanied by com…
DADA: Dialect Adaptation via Dynamic Aggregation of Linguistic Rules
Yanchen Liu, William Held, Diyi Yang
Existing large language models (LLMs) that mainly focus on Standard American English (SAE) often lead to significantly worse performance when being applied to other English dialect…
SMoA: Sparse Mixture of Adapters to Mitigate Multiple Dataset Biases
Yanchen Liu, Jing Yan, Yan Chen +2
Recent studies reveal that various biases exist in different NLP tasks, and over-reliance on biases results in models' poor generalization ability and low adversarial robustness. T…