Emptying the Ocean with a Spoon: Should We Edit Models?
Submission Type: Regular Short Paper
Submission Track: Theme Track: Large Language Models and the Future of NLP
Keywords: Model Editing, LLMs, Factual Knowledge, Continual Learning, Knowledge Representation, Opinion Paper
TL;DR: We raise concerns about the method of model editing, identifying risks it poses and highlighting alternatives.
Abstract: We call into question the recently popularized method of direct model editing as a means of correcting factual errors in LLM generations. We contrast model editing with three similar but distinct approaches that pursue better defined objectives: (1) retrieval-based architectures, which decouple factual memory from inference and linguistic capabilities embodied in LLMs; (2) concept erasure methods, which aim at preventing systemic bias in generated text; and (3) attribution methods, which aim at grounding generations into identified textual sources. We argue that direct model editing cannot be trusted as a systematic remedy for the disadvantages inherent to LLMs, and while it has proven potential in improving model explainability, it opens risks by reinforcing the notion that models can be trusted for factuality. We call for cautious promotion and application of model editing as part of the LLM deployment process, and for responsibly limiting the use cases of LLMs to those not relying on editing as a critical component.
Submission Number: 2037
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