---
title: "Implicit Bias in Peer Review — Zhang 2026"
description: "Most NLP research on bias in peer review targets explicit, predefined categories such as gender or affiliation, so subtler forms of prejudice go…"
authors: ["Xulang Zhang", "Rui Mao", "Erik Cambria"]
year: 2026
venue: "Proceedings of the Fifteenth Language Resources and Evaluation Conference"
source: "https://aclanthology.org/2026.lrec-1.752/"
doi: "10.63317/4uxwwzgswmqc"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zhang-2026-implicit-bias-peer-review-through/"
---
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# Implicit Bias in Peer Review: Through the Lens of Language Abstraction

**Authors:** [Xulang Zhang](https://md.research.elamur.ai/free/en/authors/xulang-zhang/), [Rui Mao](https://md.research.elamur.ai/free/en/authors/rui-mao/), [Erik Cambria](https://md.research.elamur.ai/free/en/authors/erik-cambria/) · **Year:** 2026 · **Venue:** Proceedings of the Fifteenth Language Resources and Evaluation Conference · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.lrec-1.752/) · [PDF](https://aclanthology.org/2026.lrec-1.752.pdf) · [DOI 10.63317/4uxwwzgswmqc](https://doi.org/10.63317/4uxwwzgswmqc) · [OpenAlex](https://openalex.org/W7160075422)

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

## Summary
Most NLP research on bias in peer review targets explicit, predefined categories such as gender or affiliation, so subtler forms of prejudice go unexamined. The authors use language abstraction as a general, category-free proxy, drawing on Linguistic Intergroup Bias (LIB) theory. Abstract wording implies stable dispositions and is hard to refute, so it can both reveal a speaker's bias and pass it on to others. They train a scorer based on pretrained language models to predict Linguistic Category Model (LCM) abstraction, replacing earlier lexicon-based coding. Training uses human-annotated data from Johnson-Grey et al. (2020). They apply the scorer to ICLR reviews from 2017–2019, after an LLM splits each review into summary, strengths, weaknesses and other sections. Statistical tests show three patterns:
- Reviews recommending rejection are more abstract than those recommending acceptance.
- Reviews of rejected papers are more abstract overall.
- Abstraction in the weaknesses section is linked to the final decision.

The authors describe this as the first application of generalized LIB to academic text. It offers a way to audit implicit bias in reviewing without predefined bias categories.

## Key points
- **LCM categories and weights:** Descriptive Action Verbs (DAV) = 1, Interpretive Action Verbs (IAV) = 2, State Verbs (SV) = 3, Adjectives/adverbs (ADJ) = 4. A sentence's abstraction score is the weighted mean of these term counts.
- **Training data (Johnson-Grey et al., 2020):**
  - Study 1 subset used for training: 1,939 samples.
  - Feature subset split into dev (718) and test (719).
  - A few samples are marked as invalid for LCM coding.
- **Analysis corpus:** the ICLR dataset (Chakraborty et al., 2020), with 8,151 reviews of 5,289 submissions from 2017–2019, including ratings and decisions. The deepseek-chat (v3) API divided each review into sections.
- **Methods by research question:** t-tests and z-tests for R1 (accept vs. reject recommendations) and R2 (accepted vs. rejected papers). ANCOVA on weaknesses-section scores for R3 (influence on meta-reviewer decisions).
- **Main finding:** reviews favouring rejection, and reviews of rejected papers, use more abstract language. Abstraction in the weaknesses section affects acceptance outcomes, which suggests possible transmission of implicit bias.

*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/en/shelves/linguistics/)

## Cite

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

```bibtex
@inproceedings{zhang2026implicit,
  title = {{Implicit Bias in Peer Review: Through the Lens of Language Abstraction}},
  author = {Zhang, Xulang and Mao, Rui and Cambria, Erik},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  year = {2026},
  doi = {10.63317/4uxwwzgswmqc},
  url = {https://doi.org/10.63317/4uxwwzgswmqc}
}
```

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