Gregor Bachmann, Vaishnavh Nagarajan
We lifted 7 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 |
|---|---|---|
| gregorbachmann/next-token-failures | canonical | 6 of 7 |
| Function | Status | Where it lives |
|---|---|---|
| accuracy | Ran | gregorbachmann/next-token-failures/utils/training_utils.py pointer only (licence: NONE) · get_code("c1f4b077a9b230f4") |
| gelu | Ran | gregorbachmann/next-token-failures/models/lib.py pointer only (licence: NONE) · get_code("272ef5f104a11d59") |
| get_lr | Ran | gregorbachmann/next-token-failures/utils/training_utils.py pointer only (licence: NONE) · get_code("1b9a2fc53c54c9be") |
| get_run_name | Ran | gregorbachmann/next-token-failures/utils/training_utils.py pointer only (licence: NONE) · get_code("e64b971fffaa3f52") |
| load_pythia | Ran | gregorbachmann/next-token-failures/utils/load.py pointer only (licence: NONE) · get_code("d8ecc47b9ccc6855") |
| remap | Ran | gregorbachmann/next-token-failures/utils/load.py pointer only (licence: NONE) · get_code("ca87fc1303002ce5") |
| load_gpt | Not yet run | gregorbachmann/next-token-failures/utils/load.py pointer only (licence: NONE) · get_code("33eb4da2120207ac") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Can a mere next-token predictor faithfully model human intelligence? We crystallize this emerging concern and correct popular misconceptions surrounding it, and advocate a simple multi-token objective. As a starting point, we argue that the two often-conflated phases of next-token predictionautoregressive inference and teacher-forced training -must be treated distinctly. The popular criticism that errors can compound during autoregressive inference, crucially assumes that teacherforcing has learned an accurate next-token predictor. This assumption sidesteps a more deep-rooted problem we expose: in certain classes of tasks, teacher-forcing can simply fail to learn an accurate next-token predictor in the first place. We describe a general mechanism of how teacherforcing can fail, and design a minimal planning task where both the Transformer and the Mamba architecture empirically fail in that manner -remarkably, despite the task being straightforward to learn. Finally, we provide preliminary evidence that this failure can be resolved using teacherless training, a simple modification using dummy tokens that predicts multiple tokens in advance. We hope this finding can ground future debates and inspire explorations beyond the next-token prediction paradigm. We make our code available under https://github.com/gregorbachmann/ Next-Token-Failures * Equal contribution 1 ETH Zürich, Switzerland 2 Google Research, US.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2403.06963")
get_code_for_paper("2403.06963")
have("2403.06963")
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