5 citations · 14 across the 11 of their papers we have counts for
12 papers
LLMs Can Better Capture Human Judgments--With the Right Prompts
Danica Dillion, Chen Cecilia Liu, Baihui Wang +5
Are large language models (LLMs) bad at capturing human judgment? Two commonly stated limitations are that LLMs fail to capture full distributions of responses, and that their judg…
Tailored Emotional LLM-Supporter: Enhancing Cultural Sensitivity
Chen Cecilia Liu, Hiba Arnaout, Nils Kovačić +2
Large language models (LLMs) show promise in offering emotional support and generating empathetic responses for individuals in distress, but their ability to deliver culturally sen…
Cultural Learning-Based Culture Adaptation of Language Models
Chen Cecilia Liu, Anna Korhonen, Iryna Gurevych
Adapting large language models (LLMs) to diverse cultural values is a challenging task, as existing LLMs often reflect the values of specific groups by default, and potentially cau…
Culturally Aware and Adapted NLP: A Taxonomy and a Survey of the State of the Art
Chen Cecilia Liu, Iryna Gurevych, Anna Korhonen
The surge of interest in "culture" in NLP has inspired much recent research, but a shared understanding of "culture" remains unclear, making it difficult to evaluate progress in th…
Early Period of Training Impacts Adaptation for Out-of-Distribution Generalization: An Empirical Study
Chen Cecilia Liu, Iryna Gurevych
Prior research shows that differences in the early period of neural network training significantly impact the performance of in-distribution (ID) data of tasks. Yet, the implicatio…
Are Multilingual LLMs Culturally-Diverse Reasoners? An Investigation into Multicultural Proverbs and Sayings
Chen Cecilia Liu, Fajri Koto, Timothy Baldwin +1
Large language models (LLMs) are highly adept at question answering and reasoning tasks, but when reasoning in a situational context, human expectations vary depending on the relev…
FUN with Fisher: Improving Generalization of Adapter-Based Cross-lingual Transfer with Scheduled Unfreezing
Chen Cecilia Liu, Jonas Pfeiffer, Ivan Vulić +1
Standard fine-tuning of language models typically performs well on in-distribution data, but suffers with generalization to distribution shifts. In this work, we aim to improve the…
One does not fit all! On the Complementarity of Vision Encoders for Vision and Language Tasks
Gregor Geigle, Chen Cecilia Liu, Jonas Pfeiffer +1
Current multimodal models, aimed at solving Vision and Language (V+L) tasks, predominantly repurpose Vision Encoders (VE) as feature extractors. While many VEs -- of different arch…
Delving Deeper into Cross-lingual Visual Question Answering
Chen Liu, Jonas Pfeiffer, Anna Korhonen +2
Visual question answering (VQA) is one of the crucial vision-and-language tasks. Yet, existing VQA research has mostly focused on the English language, due to a lack of suitable ev…
Graph Representation Learning Network via Adaptive Sampling
Anderson de Andrade, Chen Liu
Graph Attention Network (GAT) and GraphSAGE are neural network architectures that operate on graph-structured data and have been widely studied for link prediction and node classif…
DENS: A Dataset for Multi-class Emotion Analysis
Chen Liu, Muhammad Osama, Anderson de Andrade
We introduce a new dataset for multi-class emotion analysis from long-form narratives in English. The Dataset for Emotions of Narrative Sequences (DENS) was collected from both cla…
Exploring Multilingual Syntactic Sentence Representations
Chen Liu, Anderson de Andrade, Muhammad Osama
We study methods for learning sentence embeddings with syntactic structure. We focus on methods of learning syntactic sentence-embeddings by using a multilingual parallel-corpus au…