Abstract:Large language models (LLMs) are prone to hallucinate unintended text due to false or outdated knowledge.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 reverse generalization is introduced and a benchmark dubbed Bidirectional Assessment for Knowledge Editing (BAKE) is constructed to evaluate if post-edited models can recall the edited knowledge in the reverse direction of editing. We conduct extensive experiments using a variety of editing methods and LLMs. The results show that while most editing methods are able to accurately recall editing facts along the modification direction, they exhibit substantial systematic deficiencies when evaluating in the reverse direction. To further investigate the underlying causes of reversal curse and to explore potential strategies for mitigation, a detailed analysis is conducted from three perspectives. Our findings reveal that although In-Context Learning (ICL) can mitigate the reversal curse to a certain extent, it lacks continuity, is limited by the input length, and may introduce hallucinations. Therefore, combining the advantages of ICL and other editing methods is a promising direction for developing new editing paradigms.
| Comments: | Accepted by TMLR |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2310.10322 [cs.CL] |
| (or arXiv:2310.10322v3 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2310.10322 arXiv-issued DOI via DataCite |
Submission history
From: Jun-Yu Ma [view email]
[v1]
Mon, 16 Oct 2023 12:04:13 UTC (701 KB)
[v2]
Sat, 12 Oct 2024 03:31:13 UTC (1,122 KB)
[v3]
Sun, 31 May 2026 03:35:33 UTC (298 KB)