arXiv:2310.10322v3 Announce Type: replace Abstract: Large language models (LLMs) are prone to hallucinate unintended text due to false or outdated knowledge. Since retraining LLMs is resource intensive, there has been a growing interest in model editing. Despite the emergence of benchmarks and approaches, existing unidirectional editing and evaluation paradigms have failed to explore the reversal curse. In this paper, we study bidirectional language model editing, aiming to provide a rigorous evaluation to assess if edited LLMs can recall the editing knowledge bidirectionally. A metric of reve
The increasing prevalence of large language models and the significant resources required for retraining drive the immediate need for effective model editing solutions.
Improving the accuracy and reliability of LLMs through bidirectional editing is crucial for their broader application and trustworthiness, particularly as they become more integrated into critical systems.
The focus on bidirectional model editing and structured evaluation benchmarks could lead to more robust and less 'hallucinating' AI models, enhancing their general utility and reducing operational risks.
- · AI developers
- · Enterprises leveraging LLMs
- · AI safety researchers
- · Model editing tool providers
- · Companies relying on unreliable LLMs
- · Developers neglecting robust evaluation
More accurate and reliable LLMs become available for a wider range of applications, including critical decision-making systems.
Reduced need for expensive and time-consuming full model retraining, accelerating the deployment and maintenance of AI systems.
Increased public and institutional trust in AI, leading to accelerated adoption across various industries and potentially influencing regulatory frameworks.
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