---
title: "Information Representation Fairness in Long-Document"
description: "The authors build a permutation-based diagnostic on a comparable Wikipedia corpus in six languages. 16 references."
authors: ["Elias Schuhmacher", "Andrianos Michail", "Juri Opitz", "Rico Sennrich", "Simon Clematide"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.246/"
doi: "10.18653/v1/2026.findings-acl.246"
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/schuhmacher-2026-information-representation-fairness-long-document/"
---
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# Information Representation Fairness in Long-Document Embeddings: The Peculiar Interaction of Positional and Language Bias

**Authors:** Elias Schuhmacher, [Andrianos Michail](https://md.research.elamur.ai/free/en/authors/andrianos-michail/), [Juri Opitz](https://md.research.elamur.ai/free/en/authors/juri-opitz/), [Rico Sennrich](https://md.research.elamur.ai/free/en/authors/rico-sennrich/) and [Simon Clematide](https://md.research.elamur.ai/free/en/authors/simon-clematide/) · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/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.findings-acl.246/) · [PDF](https://aclanthology.org/2026.findings-acl.246.pdf) · [DOI 10.18653/v1/2026.findings-acl.246](https://doi.org/10.18653/v1/2026.findings-acl.246) · [OpenAlex](https://openalex.org/W7166797716)

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

## Summary
Embedding-based search can only surface information that a document's vector actually encodes. This paper asks whether every part of a long, multi-segment document is represented equally. The authors build a permutation-based diagnostic on a comparable Wikipedia corpus in six languages. Topic-aligned articles are concatenated in all possible orders, which separates the effect of position from the effect of content. Fairness is measured as the cosine similarity between the full-document embedding and each segment's standalone embedding, and position effects are estimated with OLS regression.

They test two multilingual long-context encoders, mGTE and jina-v3. Both models favour content placed first in the document. Their language preferences differ by model, and segments in higher-resource languages such as English tend to be favoured. Attention analysis of mGTE's \<s> pooling token links the first-position bias to attention that is concentrated on early tokens. The authors therefore propose a training-free calibration applied at inference time, which spreads attention more evenly across the document. This makes later segments more discoverable without degrading semantics. The work matters for page-level retrieval, such as historical newspapers in which independent articles share one page.

## Key points
- **Corpus construction:**
  - Wikipedia articles in English, Chinese, German, Italian, Korean and Hindi, with each segment 1,000–2,000 tokens long.
  - Non-English articles were kept only if their length was within ±70% of the English article, counted in XLM-R subtokens.
  - Documents contain n ∈ {3, 4, 5, 6} segments, up to 8,192 tokens.
- **Dataset size:** 1,002 documents for n=3, 1,008 for n=4, 1,080 for n=5, and 10,080 for n=6. Both monolingual and mixed-language configurations were used; in the mixed ones, the lead segment's language differs from the language of the later segments.
- **Models:**
  - mGTE (Alibaba-NLP/gte-multilingual-base) pools via the \<s> token.
  - jina-v3 (jinaai/jina-embeddings-v3) uses mean pooling with the text-matching LoRA adapter. For this model, information retention is also measured via late-chunking contextualized segment embeddings.
- **Statistical test:** Positional fairness is tested by OLS with position indicators and standard errors clustered by segment set. The null hypothesis is that all position coefficients β_p equal zero.
- **Calibration and release:**
  - The calibration was evaluated mainly on mGTE.
  - A control experiment suggests that it adds information about later segments rather than collapsing meaning.
  - The code is released at github.com/impresso/fair-sentence-transformers, within the Impresso project funded by SNSF and FNR.

*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: Elias Schuhmacher, Andrianos Michail, Juri Opitz, Rico Sennrich and Simon Clematide. 2026. Information Representation Fairness in Long-Document Embeddings: The Peculiar Interaction of Positional and Language Bias. Findings of the Association for Computational Linguistics: ACL 2026.

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

```bibtex
@inproceedings{schuhmacher2026information,
  title = {{Information Representation Fairness in Long-Document Embeddings: The Peculiar Interaction of Positional and Language Bias}},
  author = {Elias Schuhmacher, Andrianos Michail, Juri Opitz, Rico Sennrich and Simon Clematide},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.246},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.246}
}
```

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## References

16 references, 5 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Hongliu Cao. 2025. Writing style matters: An examination of bias and fairness in information retrieval systems. In Proceedings of the Eighteenth ACM International Conference on Web Search and Data Mining, WSDM ’25, page 336–344, New York, NY, USA. Association for Computing Machinery. [find](https://scholar.google.com/scholar?q=Hongliu+Cao%2E+2025%2E+Writing+style+matters%3A+An+examination+of+bias+and+fairness+in+information+retrieval+systems%2E+In+Proceedings+of+the+Eighteenth+ACM+International+Conference+on+Web+Search+and+Data+Min)
2. João Coelho, Bruno Martins, Joao Magalhaes, Jamie Callan, and Chenyan Xiong. 2024. Dwell in the Beginning: How Language Models Embed Long Documents for Dense Retrieval. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 370–377,… [find](https://scholar.google.com/scholar?q=Jo%C3%A3o+Coelho%2C+Bruno+Martins%2C+Joao+Magalhaes%2C+Jamie+Callan%2C+and+Chenyan+Xiong%2E+2024%2E+Dwell+in+the+Beginning%3A+How+Language+Models+Embed+Long+Documents+for+Dense+Retrieval%2E+In+Proceedings+of+the+62nd+Annu)
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8. Nelson F. Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang. 2024. [Lost in the Middle: How Language Models Use Long Contexts](https://md.research.elamur.ai/free/en/papers/liu-2023-lost-middle-language-models-use/). Transactions of the Association for Computational Linguistics, 12:157–173. Place: Cambridge, MA Publisher: MIT Press.
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*Updated 2 November 2026*