We lifted 8 functions out of this paper's own repositories and ran 6 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| eleutherai/elk-generalization | canonical | 6 of 8 |
| Function | Status | Where it lives |
|---|---|---|
| H | Ran | eleutherai/elk-generalization/elk_generalization/elk/ccs_losses.py code served (permissive licence) · get_code("1f88973b76c982a1") |
| assert_type | Ran | eleutherai/elk-generalization/elk_generalization/utils.py code served (permissive licence) · get_code("1299d3784ab1ff46") |
| bootstrap_auroc | Ran | eleutherai/elk-generalization/elk_generalization/anomaly/detect_anomaly.py code served (permissive licence) · get_code("ada0375a57f92a80") |
| ccs_squared_loss | Ran | eleutherai/elk-generalization/elk_generalization/elk/ccs_losses.py code served (permissive licence) · get_code("5377efd2b2f26e7e") |
| get_logodds | Ran | eleutherai/elk-generalization/elk_generalization/anomaly/anomaly_experiment.py code served (permissive licence) · get_code("c0626aae8fa29524") |
| register | Ran | eleutherai/elk-generalization/elk_generalization/elk/ccs_losses.py code served (permissive licence) · get_code("9e7337824581dcce") |
| encode_choice | Not yet run | eleutherai/elk-generalization/elk_generalization/utils.py code served (permissive licence) · get_code("342384bd62257687") |
| load_quirky_dataset | Not yet run | eleutherai/elk-generalization/elk_generalization/datasets/loader_utils.py code served (permissive licence) · get_code("fad9b59f979eb569") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Eliciting Latent Knowledge (ELK) aims to find patterns in a capable neural network's activations that robustly track the true state of the world, especially in hard-to-verify cases where the model's output is untrusted. To further ELK research, we introduce 12 datasets and a corresponding suite of "quirky" language models (LMs) that are finetuned to make systematic errors when answering questions if and only if the keyword "Bob" is present in the prompt. We find that, especially in middle layers, linear probes usually report an LM's knowledge independently of what the LM outputs, enabling us to elicit the correct answer despite the model's untruthful output. The best probing method (logistic regression on contrast pairs) recovers 89% of the gap in AUROC between truthful and untruthful contexts, and 75% for questions harder than those used to train the probe. We also find that a mechanistic anomaly detection approach can flag untruthful behavior with 0.95 AUROC. Our results show promise for eliciting reliable knowledge from capable but untrusted models, and facilitates future research empirically investigating ELK methods.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2312.01037")
get_code_for_paper("2312.01037")
have("2312.01037")
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