1 citations · 2 across the 9 of their papers we have counts for
Showing cs.CLShow all
3 papers · 1 filter
cs.CL2025
Can LLMs Detect Their Confabulations? Estimating Reliability in Uncertainty-Aware Language Models
Tianyi Zhou, Johanne Medina, Sanjay Chawla
Large Language Models (LLMs) are prone to generating fluent but incorrect content, known as confabulation, which poses increasing risks in multi-turn or agentic applications where…
cs.CL2025
Where to show Demos in Your Prompt: A Positional Bias of In-Context Learning
Kwesi Cobbina, Tianyi Zhou
In-context learning (ICL) is a critical emerging capability of large language models (LLMs), enabling few-shot learning during inference by including a few demonstrations (demos) i…
cs.CL2024
GenQA: Generating Millions of Instructions from a Handful of Prompts
Jiuhai Chen, Rifaa Qadri, Yuxin Wen +4
Most public instruction finetuning datasets are relatively small compared to the closed source datasets used to train industry models. To study questions about finetuning at scale,…