LLMs are bad at modelling reader context. More than once I’ve been collaboratively editing a document and found an issue in the the analysis which I asked the model to fix, then it subsequently edited the document to say something like “the result is 10, not 5, because that calculation double-counted such and such”, which is going to make no sense to a reader who will never encounter the superseded calculation. This has happened with both Fable and GPT 5.5 so it’s not an old issue that’s been fixed. Given that you want examples I will try to save the next instance of this I come across (though I’ve moved away from having LLMs directly edit English in my writing, because I think they make a mess of it too often).
EDIT: I found one!
``` class ApproxUnrolling(Serializable):
“”″Run the SOURCE (approximate unrolling) training-data attribution pipeline.
- Currently only step 1 (per-checkpoint Hessian precompute) is wired up;
- later steps land incrementally. See
+ All 8 steps run end-to-end, from per-checkpoint Hessian precompute through
+ per-segment scoring and aggregation into ``<run>/scores.npy``. See ```
The “All 8 steps run” docstring wouldn’t make much sense to anyone who didn’t know it used to say “currently only step 1 is wired up”, which is most people likely to read it.
LLMs are bad at modelling reader context. More than once I’ve been collaboratively editing a document and found an issue in the the analysis which I asked the model to fix, then it subsequently edited the document to say something like “the result is 10, not 5, because that calculation double-counted such and such”, which is going to make no sense to a reader who will never encounter the superseded calculation. This has happened with both Fable and GPT 5.5 so it’s not an old issue that’s been fixed. Given that you want examples I will try to save the next instance of this I come across (though I’ve moved away from having LLMs directly edit English in my writing, because I think they make a mess of it too often).
EDIT: I found one!
```
class ApproxUnrolling(Serializable):
“”″Run the SOURCE (approximate unrolling) training-data attribution pipeline.
- Currently only step 1 (per-checkpoint Hessian precompute) is wired up;
- later steps land incrementally. See
+ All 8 steps run end-to-end, from per-checkpoint Hessian precompute through
+ per-segment scoring and aggregation into ``<run>/scores.npy``. See
```
The “All 8 steps run” docstring wouldn’t make much sense to anyone who didn’t know it used to say “currently only step 1 is wired up”, which is most people likely to read it.