Bibliographic record
Abstract
The recently introduced path-star task is a minimal task designed to exemplify limitations to the abilities of language models (Bachmann and Nagarajan, 2024).It involves a path-star graph where multiple arms radiate from a single starting node and each node is unique.Given the start node and a specified target node that ends an arm, the task is to generate the arm containing that target node.This is straightforward for a human but surprisingly difficult for language models, which did not outperform the random baseline.The authors hypothesized this is due to a deficiency in teacher-forcing and the next-token prediction paradigm.We demonstrate the task is learnable using teacher-forcing in alternative settings and that the issue is partially due to representation.We introduce a regularization method using structured samples of the same graph but with differing target nodes, improving results across a variety of model types.We provide RASP proofs showing the task is theoretically solvable.Finally, we find settings where an encoder-only model can consistently solve the task.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.014 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".