9 papers
Harnessing Temporal Databases for Systematic Evaluation of Factual Time-Sensitive Question-Answering in Large Language Models
Soyeon Kim, Jindong Wang, Xing Xie +1
Facts change over time, making it essential for Large Language Models (LLMs) to handle time-sensitive factual knowledge accurately and reliably. Although factual Time-Sensitive Que…
Talking with Tables for Better LLM Factual Data Interactions
Jio Oh, Geon Heo, Seungjun Oh +5
Large Language Models (LLMs) often struggle with requests related to information retrieval and data manipulation that frequently arise in real-world scenarios under multiple condit…
BPL: Bias-adaptive Preference Distillation Learning for Recommender System
SeongKu Kang, Jianxun Lian, Dongha Lee +6
Recommender systems suffer from biases that cause the collected feedback to incompletely reveal user preference. While debiasing learning has been extensively studied, they mostly…
Impact of Noisy Supervision in Foundation Model Learning
Hao Chen, Zihan Wang, Ran Tao +5
Foundation models are usually pre-trained on large-scale datasets and then adapted to downstream tasks through tuning. However, the large-scale pre-training datasets, often inacces…
CultureVLM: Characterizing and Improving Cultural Understanding of Vision-Language Models for over 100 Countries
Shudong Liu, Yiqiao Jin, Cheng Li +6
Vision-language models (VLMs) have advanced human-AI interaction but struggle with cultural understanding, often misinterpreting symbols, gestures, and artifacts due to biases in p…
CultureLLM: Incorporating Cultural Differences into Large Language Models
Cheng Li, Mengzhou Chen, Jindong Wang +2
Large language models (LLMs) are reported to be partial to certain cultures owing to the training data dominance from the English corpora. Since multilingual cultural data are ofte…