Extensive previous research has focused on post-training knowledge editing (KE) for
language models (LMs) to ensure that knowledge remains accurate and up-to-date. One …

Self-consistency-based approaches, which involve repeatedly sampling multiple outputs
and selecting the most consistent one as the final response, prove to be remarkably effective …

Large language models can absorb a massive amount of knowledge through pretraining,
but pretraining is inefficient for acquiring long-tailed or specialized facts. Therefore, fine …

Large language models (LLMs) have significantly impacted many aspects of our lives.
However, assessing and ensuring their chronological knowledge remains challenging …

Language model agents excel in long-session planning and reasoning, but existing
benchmarks primarily focus on goal-oriented tasks with explicit objectives, neglecting …

Hallucinations in LLMs: Types, Causes, and Approaches for Enhanced Reliability Page 1
Hallucinations in LLMs: Types, Causes, and Approaches for Enhanced Reliability Meade …

As large language models (LLMs) demonstrate increasingly advanced capabilities, aligning
their behaviors with human values and preferences becomes crucial for their wide adoption …

Schema and entity matching tasks are crucial for data integration and management. While
large language models (LLMs) have shown promising results in these tasks, they suffer from …

Understanding how the learning of new texts alter the existing knowledge in a large
language model is of great importance, because it is through these accumulated changes …