Bibliographic record
Abstract
Transformer language models (LMs), the cornerstone of the popular large language models (LLMs), have revolutionised the fields of artificial intelligence (AI) and natural language processing (NLP). However, we still understand relatively little about their computational mechanisms and the reasons for their effectiveness. The internal workings of these models remain enigmatic "black boxes." In this thesis, I present a framework for interpreting LMs, which can be aptly described using the term "renormalization." Inherent model components — such as layers, attention heads, MLPs, or neurons — should serve as the base units of abstraction for the renormalization process. This approach is preferable to using familiar linguistic or cognitive structures, as there is insufficient evidence to suggest that the internal mechanisms of LMs operate in a manner analogous to human linguistic or cognitive frameworks. Furthermore, our analysis of the Knowledge Neuron (KN) thesis suggests that neurons exhibit a remarkable degree of idiosyncrasy in the types of function and information they associate with. This finding underscores the necessity of conducting the coarse-graining process at the granularity of individual neurons. However, our examination also reveals critical limitations of the KN thesis, suggesting that simply decomposing the model without accounting for interactions between these units provides an incomplete explanation. These interactions can be captured through circuit discovery methods. To address this, we propose a novel approach that enables joint pruning of connection edges and weight parameters, facilitating efficient neuron-level granularity. As a result, our method surpasses previous approaches, achieving state-of-the-art results in LM interpretation. Finally, our findings reveal two key insights for applications: global representational techniques that involve modifications to the entire model should be prioritised, while conventional, pre-theoretical speculation from cognitive science and linguistics has to date not been a fruitful enterprise. To demonstrate this, we present a case study on the task of temporal relation extraction, where, by leveraging these insights, we propose two effective improvements that achieve state-of-the-art performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".