1 citations · 2 across the 6 of their papers we have counts for
6 papers
Deep and shallow biases in language models
An Vo, Vy Tuong Dang, Khai-Nguyen Nguyen +4
Large language models often repeatedly select the same answer even when many alternatives are plausible. Prior work treats this concentration as bias, but it does not distinguish s…
EnComp: Lightweight Encoder-Only Context Compression for Retrieval-Augmented Question Answering
Thao Do, Dinh Phu Tran, An Vo +2
Efficient context compression is critical for retrieval-augmented question answering in resource-constrained settings, where long retrieved contexts increase latency, memory use, a…
VMMU: A Vietnamese Multitask Multimodal Understanding and Reasoning Benchmark
Vy Tuong Dang, An Vo, Emilio Villa-Cueva +4
We introduce VMMU, a Vietnamese Multitask Multimodal Understanding and Reasoning Benchmark designed to evaluate how vision-language models (VLMs) interpret and reason over visual a…
B-score: Detecting biases in large language models using response history
An Vo, Mohammad Reza Taesiri, Daeyoung Kim +1
Large language models (LLMs) often exhibit strong biases, e.g, against women or in favor of the number 7. We investigate whether LLMs would be able to output less biased answers wh…
Vision Language Models are Biased
An Vo, Khai-Nguyen Nguyen, Mohammad Reza Taesiri +3
Large language models (LLMs) memorize a vast amount of prior knowledge from the Internet that helps them on downstream tasks but also may notoriously sway their outputs towards wro…
Reference-Based Post-OCR Processing with LLM for Precise Diacritic Text in Historical Document Recognition
Thao Do, Dinh Phu Tran, An Vo +1
Extracting fine-grained OCR text from aged documents in diacritic languages remains challenging due to unexpected artifacts, time-induced degradation, and lack of datasets. While s…