5/6: Finally, we use this information as a weak oracle to trigger self-correction. Re-prompting the LM based on the probe’s prediction leads to a correction of up to 11% of the mistakes made by the model.

4/6: Can this be useful in a more realistic setting? We apply the probes trained on “pure arithmetic” queries to structured CoT traces obtained on GSM8K. The probes transfer well in a robust and consistent manner.

3/6: Given the previous results, it should be possible to predict the correctness of the model output. We designed lightweight probes that achieve high accuracy.

2/6: We feed an LM arithmetic queries and we train lightweight probes (e.g., circular) on its residual stream. Interestingly, they can accurately predict the ground-truth result, regardless of the LM's correctness.

1/6: Can we use an LLM’s hidden activations to predict and prevent wrong predictions? When it comes to arithmetic, yes!
I’m presenting new work w/
@alestolfo.bsky.social
“Probing for Arithmetic Errors in LMs” @ #ICML2025 Act Interp WS
🧵 below