Enhancing Few Shot Named Entity Recognition via Label Semantic Description and Diversity Text
Bibliographic record
Abstract
Large Language Models (LLMs) have demonstrated remarkable few-shot learning capabilities on Named Entity Recognition (NER) tasks, particularly through prompt-based approaches that avoid additional fine-tuning. However, despite the powerful generative and reasoning abilities of LLMs, two critical challenges remain: (1) semantic discrepancy of the same label across different datasets, which leads to recognition errors when using general labels to guide model outputs, and (2) contextual homogeneity in in-context examples, which limits the model’s ability to distinguish fine-grained entity types during inference. To address these challenges, we propose LSDNER, a dual-faceted prompt construction strategy that integrates structured label semantic descriptions and promotes context diversity. Specifically, to tackle label semantic inconsistency, we introduce a structured framework that organizes label semantic descriptions into definitions, attributes, relational features, and behavioral characteristics. This representation enables LLMs to better understand the dataset-specific semantic meanings of entity labels. To address contextual monotony, we devise a diversity-driven sampling strategy for selecting in-context demonstrations, thereby expanding semantic coverage and promoting reasoning capabilities. Experiments on three general and four domain-specific NER datasets demonstrate that our approach surpasses prompt-based methods and achieves competitive results with supervised baselines. Our code is available at: https://github.com/hui68633/LSDNER.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".