Fusion of Local and Global Context in Large Language Models for Text Classification
Why this work is in the frame
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Bibliographic record
Abstract
This study addresses the problem of insufficient context capture in text classification and proposes a large language model method enhanced with contextual mechanisms. At the input layer, raw text is transformed into vector sequences that incorporate both semantic and sequential features through the combination of embedding representation and positional encoding. A context encoder based on self-attention is then introduced to capture global dependencies within the sequence. At the same time, a context gating unit is designed to achieve dynamic fusion of local and global information, which preserves fine-grained features while strengthening overall contextual consistency. Furthermore, a global context aggregation module integrates semantic information across sentences or paragraphs, enhancing the model's ability to represent long texts and implicit semantics. In the output stage, sentence-level pooling is used to generate a unified representation, followed by a classification head to complete label prediction. To validate the effectiveness of the method, comparative experiments were conducted on a public news text classification dataset. The results show that the proposed method outperforms traditional deep learning models and mainstream large-model baselines in terms of accuracy, precision, recall, and F1-score. It maintains a more stable classification performance when dealing with semantic ambiguity and topic shifts. In addition, sensitivity experiments on hidden dimension settings demonstrate that moderate model capacity significantly improves performance, while excessive complexity may introduce redundant representations and slight overfitting. This study demonstrates the practical value of context enhancement mechanisms in large language models and provides a more robust and effective solution for text classification tasks.
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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 | 0.001 |
| 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 it