4 citations · 12 across the 10 of their papers we have counts for
8 papers · 1 filter
Segment-Level Attribution for Selective Learning of Long Reasoning Traces
Siyuan Wang, Yanchen Liu, Xiang Ren
Large Reasoning Models (LRMs) achieve strong reasoning performance by generating long chains of thought (CoTs), yet only a small fraction of these traces meaningfully contributes t…
PrivacyLens: Evaluating Privacy Norm Awareness of Language Models in Action
Yijia Shao, Tianshi Li, Weiyan Shi +2
As language models (LMs) are widely utilized in personalized communication scenarios (e.g., sending emails, writing social media posts) and endowed with a certain level of agency,…
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…
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…
Semantic-Oriented Unlabeled Priming for Large-Scale Language Models
Yanchen Liu, Timo Schick, Hinrich Schütze
Due to the high costs associated with finetuning large language models, various recent works propose to adapt them to specific tasks without any parameter updates through in-contex…