Similarity, Transformation and the Newly Found Invariance of Influence Functions
Bibliographic record
Abstract
Ensuring that semantic representations capture the actual meanings of sentences to the exclusion of extraneous features remains a difficult challenge despite the amazing performance of representations like sBERT. We compare and contrast the semantic-encoding behaviours of sentence embeddings as well as influence functions, a resurgent method in the field of language model intepretability, using meaning-preserving grammatical transformations. Under the two tasks of sentence similarity and a new task called entity invariance, we seek to understand how these two measures of semantics warp under surface-level syntactic changes. Invariance to meaning-preserving transformations is an important aspect in which sentence embeddings and influence functions seem to differ. Nevertheless, our experiments find that across all our tasks and transformations, sentence embeddings and influence functions are highly correlated. We conclude that there is evidence that influence functions point towards a deeper encoding of semantics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".