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Unmasking Deceptive Visuals: Benchmarking Multimodal Large Language Models on Misleading Chart Question Answering
Zixin Chen, Sicheng Song, Kashun Shum +4
Misleading visualizations, which manipulate chart representations to support specific claims, can distort perception and lead to incorrect conclusions. Despite decades of research,…
Predictive Data Selection: The Data That Predicts Is the Data That Teaches
Kashun Shum, Yuzhen Huang, Hongjian Zou +5
Language model pretraining involves training on extensive corpora, where data quality plays a pivotal role. In this work, we aim to directly estimate the contribution of data durin…
FIRST: Teach A Reliable Large Language Model Through Efficient Trustworthy Distillation
KaShun Shum, Minrui Xu, Jianshu Zhang +5
Large language models (LLMs) have become increasingly prevalent in our daily lives, leading to an expectation for LLMs to be trustworthy -- - both accurate and well-calibrated (the…
RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models
Cheng Niu, Yuanhao Wu, Juno Zhu +5
Retrieval-augmented generation (RAG) has become a main technique for alleviating hallucinations in large language models (LLMs). Despite the integration of RAG, LLMs may still pres…
LMFlow: An Extensible Toolkit for Finetuning and Inference of Large Foundation Models
Shizhe Diao, Rui Pan, Hanze Dong +4
Foundation models have demonstrated a great ability to achieve general human-level intelligence far beyond traditional approaches. As the technique keeps attracting attention from…
Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data
KaShun Shum, Shizhe Diao, Tong Zhang
Chain-of-thought (CoT) advances the reasoning abilities of large language models (LLMs) and achieves superior performance in complex reasoning tasks. However, most CoT studies rely…