2 citations · 13 across the 34 of their papers we have counts for
8 papers · 1 filter
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…
A Unified Data Augmentation Framework for Low-Resource Multi-Domain Dialogue Generation
Yongkang Liu, Ercong Nie, Shi Feng +5
Current state-of-the-art dialogue systems heavily rely on extensive training datasets. However, challenges arise in domains where domain-specific training datasets are insufficient…
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:…
Decoding Probing: Revealing Internal Linguistic Structures in Neural Language Models using Minimal Pairs
Linyang He, Peili Chen, Ercong Nie +2
Inspired by cognitive neuroscience studies, we introduce a novel `decoding probing' method that uses minimal pairs benchmark (BLiMP) to probe internal linguistic characteristics in…
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…
Why Lift so Heavy? Slimming Large Language Models by Cutting Off the Layers
Shuzhou Yuan, Ercong Nie, Bolei Ma +1
Large Language Models (LLMs) possess outstanding capabilities in addressing various natural language processing (NLP) tasks. However, the sheer size of these models poses challenge…
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…