---
title: "Reverse Probing — Anikina 2025"
description: "The authors invert the classic probing setup. Usually, the representations of a model trained on a complex task are tested on several simple ones. Here…"
authors: ["Tatiana Anikina", "Arne Binder", "David Harbecke", "Stalin Varanasi", "Leonhard Hennig", "Simon Ostermann", "Sebastian Möller", "Josef van Genabith"]
year: 2025
venue: "Proceedings of the 10th Workshop on Representation Learning for NLP (RepL4NLP-2025)"
source: "https://aclanthology.org/2025.repl4nlp-1.9/"
doi: "10.18653/v1/2025.repl4nlp-1.9"
arxiv: "2501.19316"
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/papers/anikina-2025-reverse-probing-evaluating-knowledge-transfer/"
---
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# Reverse Probing: Evaluating Knowledge Transfer via Finetuned Task Embeddings for Coreference Resolution

**Authors:** [Tatiana Anikina](https://md.research.elamur.ai/free/authors/tatiana-anikina/), Arne Binder, David Harbecke, Stalin Varanasi, Leonhard Hennig, [Simon Ostermann](https://md.research.elamur.ai/free/authors/simon-ostermann/), [Sebastian Möller](https://md.research.elamur.ai/free/authors/sebastian-moller/), [Josef van Genabith](https://md.research.elamur.ai/free/authors/josef-van-genabith/) · **Year:** 2025 · **Venue:** Proceedings of the 10th Workshop on Representation Learning for NLP (RepL4NLP-2025) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2025.repl4nlp-1.9/) · [PDF](https://aclanthology.org/2025.repl4nlp-1.9.pdf) · [DOI 10.18653/v1/2025.repl4nlp-1.9](https://doi.org/10.18653/v1/2025.repl4nlp-1.9) · [arXiv 2501.19316](https://arxiv.org/abs/2501.19316) · [OpenAlex](https://openalex.org/W4411119205)

**Citations:** 0 citations (openalex-snapshot · 2026-10-14)

## Summary

The authors invert the classic probing setup. Usually, the representations of a model trained on a complex task are tested on several simple ones. Here it is the other way around: embeddings are taken from several models fine-tuned on simpler tasks, and the authors examine how useful they are for a single complex target task, coreference resolution. All sources are frozen variants of bert-base-cased fine-tuned on MRPC (paraphrase), NER, relation extraction (TACRED) and extractive QA (SQuAD 2.0). Plain BERT is also included for comparison. On top of their token representations, only a coreference head is trained, following the coref-hoi implementation on OntoNotes CoNLL 2012. The study covers layer choice, L2 normalization, concatenation of multiple layers, and aggregation of several models via averaging or simple attention. MRPC, a semantic similarity task, turned out to be the most useful source, while NER, contrary to expectations, gave the worst result. Intermediate layers are often better than the last one, and a combination of several models usually outperforms a single one, especially with attention-based aggregation. The work shows how to reuse already trained representations without full fine-tuning, which saves computation.

## Key points

- Upper bound: a fully fine-tuned coreference model achieves 73.75 average F1 (MUC, B³, CEAF).
- In a single run, the best were MRPC (64.32 F1) and plain BERT (64.01). They are followed by RE (52.43), QA (47.51) and NER (36.03).
- In their conclusions for individual models, the authors report a range from 35.63 F1 (NER) to 61.16 F1 (MRPC).
- The best embeddings often come from lower layers rather than the last ones. For a single model, concatenating the outputs of the last four layers works best, while concatenating all layers gives no advantage.
- The code is published in the Cora4NLP/multi-task-knowledge-transfer repository on GitHub. Among the limitations, the authors note that all models are BERT-based and English-only.

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

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/shelves/linguistics/)

Topics: [Named entity recognition](https://md.research.elamur.ai/free/topics/named-entity-recognition/) · [Coreference resolution](https://md.research.elamur.ai/free/topics/coreference-resolution/)

## Cite

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

```bibtex
@inproceedings{anikina2025reverse,
  title = {{Reverse Probing: Evaluating Knowledge Transfer via Finetuned Task Embeddings for Coreference Resolution}},
  author = {Anikina, Tatiana and Binder, Arne and Harbecke, David and Varanasi, Stalin and Hennig, Leonhard and Ostermann, Simon and Möller, Sebastian and Genabith, Josef van},
  booktitle = {Proceedings of the 10th Workshop on Representation Learning for NLP (RepL4NLP-2025)},
  year = {2025},
  doi = {10.18653/v1/2025.repl4nlp-1.9},
  eprint = {2501.19316},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2025.repl4nlp-1.9}
}
```

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