8 citations · 14 across the 9 of their papers we have counts for
5 papers · 1 filter
Rethinking ChatGPT's Success: Usability and Cognitive Behaviors Enabled by Auto-regressive LLMs' Prompting
Xinzhe Li, Ming Liu
Over the last decade, a wide range of training and deployment strategies for Large Language Models (LLMs) have emerged. Among these, the prompting paradigms of Auto-regressive LLMs…
GRAMMAR: Grounded and Modular Methodology for Assessment of Closed-Domain Retrieval-Augmented Language Model
Xinzhe Li, Ming Liu, Shang Gao
Retrieval-Augmented Generation (RAG) systems are widely used across various industries for querying closed-domain and in-house knowledge bases. However, evaluating these systems pr…
Make Text Unlearnable: Exploiting Effective Patterns to Protect Personal Data
Xinzhe Li, Ming Liu, Shang Gao
This paper addresses the ethical concerns arising from the use of unauthorized public data in deep learning models and proposes a novel solution. Specifically, building on the work…
Can Pretrained Language Models Derive Correct Semantics from Corrupt Subwords under Noise?
Xinzhe Li, Ming Liu, Shang Gao
For Pretrained Language Models (PLMs), their susceptibility to noise has recently been linked to subword segmentation. However, it is unclear which aspects of segmentation affect t…
A Survey on Out-of-Distribution Evaluation of Neural NLP Models
Xinzhe Li, Ming Liu, Shang Gao +1
Adversarial robustness, domain generalization and dataset biases are three active lines of research contributing to out-of-distribution (OOD) evaluation on neural NLP models. Howev…