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cs.CL2024

Graph-Guided Textual Explanation Generation Framework

Shuzhou Yuan, Jingyi Sun, Ran Zhang +4

Natural language explanations (NLEs) are commonly used to provide plausible free-text explanations of a model's reasoning about its predictions. However, recent work has questioned…

cs.CL2024

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-…

cs.CL2024

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…

cs.CL2024

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…

cs.CL2024

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…