55 citations · 111 across the 21 of their papers we have counts for
11 papers · 1 filter
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…
Can I understand what I create? Self-Knowledge Evaluation of Large Language Models
Zhiquan Tan, Lai Wei, Jindong Wang +2
Large language models (LLMs) have achieved remarkable progress in linguistic tasks, necessitating robust evaluation frameworks to understand their capabilities and limitations. Ins…
CulturePark: Boosting Cross-cultural Understanding in Large Language Models
Cheng Li, Damien Teney, Linyi Yang +3
Cultural bias is pervasive in many large language models (LLMs), largely due to the deficiency of data representative of different cultures. Typically, cultural datasets and benchm…
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…
ERBench: An Entity-Relationship based Automatically Verifiable Hallucination Benchmark for Large Language Models
Jio Oh, Soyeon Kim, Junseok Seo +4
Large language models (LLMs) have achieved unprecedented performances in various applications, yet evaluating them is still challenging. Existing benchmarks are either manually con…
KIEval: A Knowledge-grounded Interactive Evaluation Framework for Large Language Models
Zhuohao Yu, Chang Gao, Wenjin Yao +6
Automatic evaluation methods for large language models (LLMs) are hindered by data contamination, leading to inflated assessments of their effectiveness. Existing strategies, which…
Dynamic Evaluation of Large Language Models by Meta Probing Agents
Kaijie Zhu, Jindong Wang, Qinlin Zhao +2
Evaluation of large language models (LLMs) has raised great concerns in the community due to the issue of data contamination. Existing work designed evaluation protocols using well…
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…
SpecFormer: Guarding Vision Transformer Robustness via Maximum Singular Value Penalization
Xixu Hu, Runkai Zheng, Jindong Wang +3
Vision Transformers (ViTs) are increasingly used in computer vision due to their high performance, but their vulnerability to adversarial attacks is a concern. Existing methods lac…
A General Framework for Learning from Weak Supervision
Hao Chen, Jindong Wang, Lei Feng +6
Weakly supervised learning generally faces challenges in applicability to various scenarios with diverse weak supervision and in scalability due to the complexity of existing algor…
On Catastrophic Inheritance of Large Foundation Models
Hao Chen, Bhiksha Raj, Xing Xie +1
Large foundation models (LFMs) are claiming incredible performances. Yet great concerns have been raised about their mythic and uninterpreted potentials not only in machine learnin…