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Findings
- IC-1206GPT-2-XL, GPT-J, Falcon-7B, Llama-2-7B, and Llama-2-13B are vulnerable to backdoor injection via lightweight parameter editing with only 15 samples, achieving near-100% attack success rate while preserving clean performance [eval]
- IC-348Sequential parameter-modifying editing causes progressive degradation of general abilities in GPT-2 XL, Llama-2 7B, and Llama-3 8B, driven by growth in the condition number of the edited matrix [eval]
- IC-381Individual knowledge is not parameter-localizable in GPT-J: existing localization methods (KN, ROME, KC) are neither faithful nor reliable [eval]
- IC-409Knowledge editing methods correct verified hallucinations in Llama2-7B, Llama3-8B, and Mistral-v0.3-7B far less effectively than their scores on existing benchmarks suggest [eval]
- IC-637KN edit (neuron suppression) has low reliability, overturning at most 5.2% of BLIMP categorical predictions and achieving only 1.66%–47.86% reliability on factual tasks [eval]
- IC-715Factual information deleted from GPT-J, LLaMA-2, and GPT-2-XL via ROME or MEMIT remains linearly recoverable from intermediate hidden states, with up to 89% extraction success at budget b=20 [eval]
- IC-716Factual information deleted from GPT-J, LLaMA-2, and GPT-2-XL via ROME or MEMIT is recoverable by sampling outputs on automatically generated rephrased prompts, with up to 56% extraction success at budget b=20 [eval]
- IC-903Knowledge editing performance (ES, GS, LS) improves as model scale increases from GPT-2 (124M) to T5-XL (2.8B) to GPT-J (6B) across all editing methods [eval]