263 citations · 263 across the 6 of their papers we have counts for
6 papers
Inverse IFEval: Can LLMs Unlearn Stubborn Training Conventions to Follow Real Instructions?
Qinyan Zhang, Xinping Lei, Ruijie Miao +18
Large Language Models (LLMs) achieve strong performance on diverse tasks but often exhibit cognitive inertia, struggling to follow instructions that conflict with the standardized…
LingBench++: A Linguistically-Informed Benchmark and Reasoning Framework for Multi-Step and Cross-Cultural Inference with LLMs
Da-Chen Lian, Ri-Sheng Huang, Pin-Er Chen +7
We propose LingBench++, a linguistically-informed benchmark and reasoning framework designed to evaluate large language models (LLMs) on complex linguistic tasks inspired by the In…
Delusions of Large Language Models
Hongshen Xu, Zixv yang, Zichen Zhu +7
Large Language Models often generate factually incorrect but plausible outputs, known as hallucinations. We identify a more insidious phenomenon, LLM delusion, defined as high beli…
Retrieval Or Holistic Understanding? Dolce: Differentiate Our Long Context Evaluation Tasks
Zi Yang
We argue that there are two major distinct capabilities in long context understanding: retrieval and holistic understanding. Understanding and further improving LLMs' long context…
Towards a Human-like Open-Domain Chatbot
Daniel Adiwardana, Minh-Thang Luong, David R. So +8
We present Meena, a multi-turn open-domain chatbot trained end-to-end on data mined and filtered from public domain social media conversations. This 2.6B parameter neural network i…
A Base Camp for Scaling AI
C. J. C. Burges, T. Hart, Z. Yang +4
Modern statistical machine learning (SML) methods share a major limitation with the early approaches to AI: there is no scalable way to adapt them to new domains. Human learning so…