Core Concepts
This paper pioneers the investigation of editing conceptual knowledge for Large Language Models, highlighting the challenges and potential distortions in modifying concept-level definitions.
Abstract
The content delves into the exploration of editing conceptual knowledge for Large Language Models (LLMs). It introduces a new benchmark dataset, ConceptEdit, and evaluates existing editing methods. The study reveals limitations in modifying conceptual knowledge and emphasizes the need for further research to enhance understanding and techniques in this area.
The emergence of Large Language Models (LLMs) has led to a surge in interest in knowledge editing methods to address challenges such as misinformation and outdated knowledge. Existing approaches focus on instance-level editing but lack efficiency in modifying concepts directly. The paper introduces ConceptEdit, a novel benchmark dataset constructed from DBpedia Ontology, to evaluate the efficacy of current editing baselines on conceptual knowledge editing.
Experimental results show that while existing methods can efficiently modify concept-level definitions to some extent, they struggle with maintaining consistency at the concept-specific level. The study highlights the complexities involved in editing conceptual knowledge for LLMs and calls for advancements in understanding how these models learn and update concepts.
Key metrics like Instance Change and Concept Consistency are introduced to assess modifications at both instance and concept levels. The analysis reveals discrepancies between reliability and concept consistency metrics, emphasizing the need for more sensitive evaluation measures tailored to conceptual knowledge editing tasks.
Overall, the study sheds light on the challenges and limitations of current knowledge editing methods for LLMs, underscoring the importance of further research to enhance capabilities in modifying conceptual knowledge effectively.
Stats
Recent experiments reveal that existing editing baselines can reach high reliability but yield poor performance on concept-specific metrics.
FT method shows notable reliability but limits to smaller model GPT2-XL.
ROME leads to clear variations on instance change.
MEMIT exhibits less impact on out-of-scope neighbors.
PROMPT stands out for its generalization capabilities post-editing.
Quotes
"We anticipate this can inspire further progress in better understanding LLMs."
"Recent experiments reveal that existing editing baselines can reach high reliability but yield poor performance on concept-specific metrics."