26 citations · 74 across the 49 of their papers we have counts for
7 papers · 1 filter
Neuron-Level Analysis of Cultural Understanding in Large Language Models
Taisei Yamamoto, Ryoma Kumon, Danushka Bollegala +1
As large language models (LLMs) are increasingly deployed worldwide, ensuring their fair and comprehensive cultural understanding is important. However, LLMs exhibit cultural bias…
Bias Mitigation or Cultural Commonsense? Evaluating LLMs with a Japanese Dataset
Taisei Yamamoto, Ryoma Kumon, Danushka Bollegala +1
Large language models (LLMs) exhibit social biases, prompting the development of various debiasing methods. However, debiasing methods may degrade the capabilities of LLMs. Previou…
Annotating Training Data for Conditional Semantic Textual Similarity Measurement using Large Language Models
Gaifan Zhang, Yi Zhou, Danushka Bollegala
Semantic similarity between two sentences depends on the aspects considered between those sentences. To study this phenomenon, Deshpande et al. (2023) proposed the Conditional Sema…
SCDTour: Embedding Axis Ordering and Merging for Interpretable Semantic Change Detection
Taichi Aida, Danushka Bollegala
In Semantic Change Detection (SCD), it is a common problem to obtain embeddings that are both interpretable and high-performing. However, improving interpretability often leads to…
Evaluating the Evaluation of Diversity in Commonsense Generation
Tianhui Zhang, Bei Peng, Danushka Bollegala
In commonsense generation, given a set of input concepts, a model must generate a response that is not only commonsense bearing, but also capturing multiple diverse viewpoints. Num…
CASE -- Condition-Aware Sentence Embeddings for Conditional Semantic Textual Similarity Measurement
Gaifan Zhang, Yi Zhou, Danushka Bollegala
The meaning conveyed by a sentence often depends on the context in which it appears. Despite the progress of sentence embedding methods, it remains unclear as how to best modify a…
Evaluating the Effect of Retrieval Augmentation on Social Biases
Tianhui Zhang, Yi Zhou, Danushka Bollegala
Retrieval Augmented Generation (RAG) has gained popularity as a method for conveniently incorporating novel facts that were not seen during the pre-training stage in Large Language…