Distilling Structured Knowledge for Text-Based Relational Reasoning
Bibliographic record
Abstract
There is an increasing interest in developing text-based relational reasoning systems, which are capable of systematically reasoning about the relationships between entities mentioned in a text. However, there remains a substantial performance gap between NLP models for relational reasoning and models based on graph neural networks (GNNs), which have access to an underlying symbolic representation of the text. In this work, we investigate how the structured knowledge of a GNN can be distilled into various NLP models in order to improve their performance. We first pre-train a GNN on a reasoning task using structured inputs and then incorporate its knowledge into an NLP model (e.g., an LSTM) via knowledge distillation. To overcome the difficulty of crossmodal knowledge transfer, we also employ a contrastive learning based module to align the latent representations of NLP models and the GNN. We test our approach with two state-ofthe-art NLP models on 12 different inductive reasoning datasets from the CLUTRR benchmark and obtain significant improvements.
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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 | 0.000 |
| Open science | 0.000 | 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".