Summary of Understanding In-context Learning with a Pelican Soup Framework, by Ting-rui Chiang et al.
Understanding In-Context Learning with a Pelican Soup Frameworkby Ting-Rui Chiang, Dani YogatamaFirst submitted to arxiv…
Understanding In-Context Learning with a Pelican Soup Frameworkby Ting-Rui Chiang, Dani YogatamaFirst submitted to arxiv…
Comparing Hallucination Detection Metrics for Multilingual Generationby Haoqiang Kang, Terra Blevins, Luke ZettlemoyerFirst submitted to…
Large Language Models as Zero-shot Dialogue State Tracker through Function Callingby Zekun Li, Zhiyu Zoey…
Can We Verify Step by Step for Incorrect Answer Detection?by Xin Xu, Shizhe Diao, Can…
Evaluating and Improving Continual Learning in Spoken Language Understandingby Muqiao Yang, Xiang Li, Umberto Cappellazzo,…
Strong hallucinations from negation and how to fix themby Nicholas Asher, Swarnadeep BharFirst submitted to…
Threads of Subtlety: Detecting Machine-Generated Texts Through Discourse Motifsby Zae Myung Kim, Kwang Hee Lee,…
InSaAF: Incorporating Safety through Accuracy and Fairness | Are LLMs ready for the Indian Legal…
Efficiency at Scale: Investigating the Performance of Diminutive Language Models in Clinical Tasksby Niall Taylor,…
When “Competency” in Reasoning Opens the Door to Vulnerability: Jailbreaking LLMs via Novel Complex Ciphersby…