---
title: "Knowledge-driven Augmentation and Retrieval for Integrative"
description: "The authors propose KARITA, a data-centric and model-agnostic framework with three modules: Shift detection: flags… 34 references."
authors: ["Weisi Liu", "Guangzeng Han", "Xiaolei Huang"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.1672/"
doi: "10.18653/v1/2026.acl-long.1672"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/liu-2026-knowledge-driven-augmentation-retrieval-integrative/"
---
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# Knowledge-driven Augmentation and Retrieval for Integrative Temporal Adaptation

**Authors:** Weisi Liu, [Guangzeng Han](https://md.research.elamur.ai/free/en/authors/guangzeng-han/) and [Xiaolei Huang](https://md.research.elamur.ai/free/en/authors/xiaolei-huang/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.1672/) · [PDF](https://aclanthology.org/2026.acl-long.1672.pdf) · [DOI 10.18653/v1/2026.acl-long.1672](https://doi.org/10.18653/v1/2026.acl-long.1672) · [OpenAlex](https://openalex.org/W7166880897)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Classifiers are usually trained on older data but used on newer data, where vocabulary, conventions and domain knowledge drift. Prior temporal-adaptation work typically tracks one kind of change, such as embedding distance or word-sense drift. That can miss shifts of several kinds that overlap. The authors propose KARITA, a data-centric and model-agnostic framework with three modules:

- **Shift detection:** flags target-time samples using three signals: predictive uncertainty, feature-level deviation, and ontology-based terminology drift.
- **Source backtracking retrieval:** pulls semantically similar labeled examples from the historical source data.
- **Knowledge-driven augmentation:** rewrites the retrieved examples with synonyms from curated thesauri (e.g., MeSH) and LLM-generated terminology mappings, then uses them to adapt the model.

KARITA was tested on multi-label classification in three domains: clinical (MIMIC-IV-Notes), legal (EurLex) and scientific (arXiv-CS). It outperformed strong temporal-adaptation baselines across all three. Ablations show that combining the three shift signals beats any single detector. This suggests that injecting domain knowledge is an effective way to keep deployed models reliable as language evolves.

## Key points
- **Datasets:**
  - MIMIC-IV-Notes: 331,794 discharge summaries, 2008–2019, top 50 ICD-10 codes (ICD-9 labels mapped to ICD-10).
  - EurLex: 65,000 EU laws, 1958–2016, 21 top-level EuroVoc categories.
  - arXiv-CS: 622,419 abstracts, 1991–2025, top 30 CS categories.
- **Detector details:**
  - Uncertainty uses maximum sigmoid probability (τp = 0.5) combined with average binary entropy (τH = 0.25).
  - Feature shift uses a normalized Mahalanobis distance computed against source-embedding statistics.
- **Full-system macro-F1:** 52.12 (MIMIC), 56.15 (EurLex), 49.82 (arXiv-CS).
- **Ablation results:**
  - The feature-only detector is weakest on EurLex and arXiv-CS; on arXiv-CS its macro-F1 falls to 23.00.
  - The uncertainty-only detector is weakest on MIMIC (42.45).
  - The ontology-based detector is the most stable across datasets.
- **Limitations and code:** the method depends on external ontologies or LLMs, and it does not model newly emerging concepts or changing label definitions. Code is available at github.com/trust-nlp/TemporalLearning-KARITA.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Weisi Liu, Guangzeng Han and Xiaolei Huang. 2026. Knowledge-driven Augmentation and Retrieval for Integrative Temporal Adaptation. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{liu2026knowledge,
  title = {{Knowledge-driven Augmentation and Retrieval for Integrative Temporal Adaptation}},
  author = {Weisi Liu, Guangzeng Han and Xiaolei Huang},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1672},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1672}
}
```

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- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/34/)

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*Updated 1 November 2026*