Pre-trained Language Models (PLMs) which are trained on large text corpus via self-
supervised learning method, have yielded promising performance on various tasks in …

This survey provides an in-depth analysis of knowledge conflicts for large language models
(LLMs), highlighting the complex challenges they encounter when blending contextual and …

Language models (LMs) with less than 100B parameters are known to perform poorly on
chain-of-thought (CoT) reasoning in contrast to large LMs when solving unseen tasks. In this …

Large language models (LLMs) often encounter knowledge conflicts, scenarios where
discrepancy arises between the internal parametric knowledge of LLMs and non-parametric …

We consider the problem of eliciting compositional generalization capabilities in large
language models (LLMs) with a novel type of prompting strategy. Compositional …

Although achieving great success, Large Language Models (LLMs) usually suffer from
unreliable hallucinations. In this paper, we define a new task of Knowledge-aware …

Large multimodal models (LMMs) excel in adhering to human instructions. However, self-
contradictory instructions may arise due to the increasing trend of multimodal interaction and …

Although large-scale pre-trained language models (PTLMs) are shown to encode rich
knowledge in their model parameters, the inherent knowledge in PTLMs can be opaque or …

Abstract Large-scale Language Models (LLMs) utilizing the Chain-of-Thought prompting
demonstrate exceptional performance in a variety of tasks. However, the persistence of …

Abstract Knowledge-intensive generation tasks like generative question answering require
models to retrieve appropriate passages from external knowledge sources to support …