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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-1261LLaMA-2 attention to constraint tokens correlates with factual correctness, and a linear probe on these attention weights predicts factual errors comparably to model confidence [eval]
- IC-1263LLaMA-2 7B and 13B attention signal for predicting factual errors is available by approximately 50% of layers, enabling early stopping without performance degradation, while LLaMA-2 70B shows a slight performance drop [eval]
- IC-1553GPT-J, GPT-2-XL, and Llama-13B decode approximately 48% of tested relations via a linear transformation on the subject representation, and this structure causally influences predictions [source]
- IC-1584LLaMA-7B and GPT-J-6B fail to interpret textual emphasis markers, with marked prompting degrading performance substantially [eval]
- IC-1586In LLaMA-7B, steering all attention heads degrades JSON format accuracy below zero-shot, while steering a subset of 50-100 heads selected via multi-task profiling raises it to 96.64; performance varies dramatically across the 32 layers and individual heads [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-349Larger LLMs (Llama-2 7B, Llama-3 8B) suffer more severe general ability degradation than smaller models (GPT-2 XL 1.5B) under the same number of sequential edits [eval]
- IC-381Individual knowledge is not parameter-localizable in GPT-J: existing localization methods (KN, ROME, KC) are neither faithful nor reliable [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-7951D subspaces of MLP activations found by DAS in GPT-2 Small (IOI) and GPT-2 XL (factual recall) produce apparent causal effects that are interpretability illusions driven by causally disconnected components activating dormant pathways [eval]