2 citations · 3 across the 11 of their papers we have counts for
5 papers · 2 filters
Large Language Models as Neurolinguistic Subjects: Discrepancy between Performance and Competence
Linyang He, Ercong Nie, Helmut Schmid +3
This study investigates the linguistic understanding of Large Language Models (LLMs) regarding signifier (form) and signified (meaning) by distinguishing two LLM assessment paradig…
BMIKE-53: Investigating Cross-Lingual Knowledge Editing with In-Context Learning
Ercong Nie, Bo Shao, Zifeng Ding +3
This paper introduces BMIKE-53, a comprehensive benchmark for cross-lingual in-context knowledge editing (IKE) across 53 languages, unifying three knowledge editing (KE) datasets:…
Decomposed Prompting: Probing Multilingual Linguistic Structure Knowledge in Large Language Models
Ercong Nie, Shuzhou Yuan, Bolei Ma +4
Probing the multilingual knowledge of linguistic structure in LLMs, often characterized as sequence labeling, faces challenges with maintaining output templates in current text-to-…
GNNavi: Navigating the Information Flow in Large Language Models by Graph Neural Network
Shuzhou Yuan, Ercong Nie, Michael Färber +2
Large Language Models (LLMs) exhibit strong In-Context Learning (ICL) capabilities when prompts with demonstrations are used. However, fine-tuning still remains crucial to further…
ToPro: Token-Level Prompt Decomposition for Cross-Lingual Sequence Labeling Tasks
Bolei Ma, Ercong Nie, Shuzhou Yuan +4
Prompt-based methods have been successfully applied to multilingual pretrained language models for zero-shot cross-lingual understanding. However, most previous studies primarily f…