3 accepted papers
Despite the remarkable capabilities of large language models, current training paradigms inadvertently foster sycophancy—alignment with user-provided information, regardless of factual accuracy. In this paper, we introduce SMART (Sycophancy Mitigation through Adaptive Reasoning Trajectories), reconc
Despite their vast capabilities, Large Language Models (LLMs) often struggle with generating reliable outputs, frequently producing high-confidence inaccuracies known as hallucinations. Addressing this challenge, our research introduces InternalInspector, a novel framework designed to enhance confid…
Memory Editing (ME) has emerged as an efficient method to modify erroneous facts or inject new facts into Large Language Models (LLMs). Two mainstream ME methods exist: parameter-modifying ME and parameter-preserving ME (integrating extra modules while preserving original parameters). Regrettably, p…