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HFEPX · Eval paper review

The Missing Minimal Pair: Stereotype Evaluation in LLMs

Nataliya Stepanova, Ivan Titov, Emily Allaway, Björn Ross

Published

Oct 6, 2026

Citations

0

Trust level

Low

Usefulness score

40/100 (Low)

Extraction confidence

45% (Low)

Derived from extracted protocol signals and abstract evidence.

Rater population

Not reported

Signals refreshed

Oct 6, 2026

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Tags

Coding Pairwise Preference

Should you rely on this paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Use this as background context only. Do not make protocol decisions from this page alone.

Best use

Background context only

Use if you need

Background context only.

What to verify

Read the full paper before copying any benchmark, metric, or protocol choices.

Main weakness

The available metadata is too thin to trust this as a primary source.

Human feedback signal
Detected
From extracted signals
Evaluation signal
Weak or implicit
Validate from full paper
Usefulness for eval research
40/100
Adjacent candidate

Treat as adjacent context, not a core eval-method reference.

If you are doing eval pipeline work, start here

Human Eval Papers LLM-as-Judge Papers Pairwise Preference Papers Tool-Use Evaluation Search

Abstract

A common approach to measuring bias in Large Language Models is to compare the log-likelihoods of two contrastive stereotype sentences. We argue that such single-pair comparisons are often unreliable: simply rewriting the same stereotype with an alternative attribute can yield logically inconsistent preferences. To address this, we propose a dual minimal pair setup that introduces two axes of comparison for robust stereotype evaluation. First, we present a data-augmentation framework that fills critical gaps in existing stereotype datasets by generating paraphrases and alternate attributes. We apply our framework on a set of English, Russian, Spanish and Chinese stereotypes. Second, we introduce two evaluation metrics tailored to the dual minimal pair setup. One of these metrics provides a new perspective on bias by modeling the mutual information (MI) between social groups and stereotyped attributes. This MI-based metric is better suited for aggregation and enables more robust comparisons of stereotype strength across different languages and models. Our code is available at https://github.com/stepanat/missing-minimal-pair/.

What we could verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

partial

Pairwise Preference

Directly usable for protocol triage.

"A common approach to measuring bias in Large Language Models is to compare the log-likelihoods of two contrastive stereotype sentences."

Evaluation Modes

missing

None explicit

Validate eval design from full paper text.

"A common approach to measuring bias in Large Language Models is to compare the log-likelihoods of two contrastive stereotype sentences."

Quality Controls

missing

Not reported

No explicit QC controls found.

"A common approach to measuring bias in Large Language Models is to compare the log-likelihoods of two contrastive stereotype sentences."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"A common approach to measuring bias in Large Language Models is to compare the log-likelihoods of two contrastive stereotype sentences."

Reported Metrics

missing

Not extracted

No metric anchors detected.

"A common approach to measuring bias in Large Language Models is to compare the log-likelihoods of two contrastive stereotype sentences."

Benchmarks and datasets

No benchmark or dataset names were extracted from the available abstract.

Reported metrics

No metric terms were extracted from the available abstract.

Human feedback details
Uses human feedback
Yes
Feedback types
Pairwise Preference
Rater population
Not reported
Expertise required
Coding
Evaluation details
Evaluation modes
None
Agentic eval
None
Quality controls
Not reported
Evidence quality
Low
Use this page as
Background context only

Research brief

Metadata summary

A common approach to measuring bias in Large Language Models is to compare the log-likelihoods of two contrastive stereotype sentences.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key takeaways

  • A common approach to measuring bias in Large Language Models is to compare the log-likelihoods of two contrastive stereotype sentences.
  • We argue that such single-pair comparisons are often unreliable: simply rewriting the same stereotype with an alternative attribute can yield logically inconsistent preferences.
  • To address this, we propose a dual minimal pair setup that introduces two axes of comparison for robust stereotype evaluation.

Researcher actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Validate inferred eval signals (Automatic metrics) against the full paper.
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Recommended queries

Contribution summary

  • To address this, we propose a dual minimal pair setup that introduces two axes of comparison for robust stereotype evaluation.
  • First, we present a data-augmentation framework that fills critical gaps in existing stereotype datasets by generating paraphrases and alternate attributes.
  • Second, we introduce two evaluation metrics tailored to the dual minimal pair setup.

Why it matters for eval

  • To address this, we propose a dual minimal pair setup that introduces two axes of comparison for robust stereotype evaluation.
  • Second, we introduce two evaluation metrics tailored to the dual minimal pair setup.

Researcher checklist

  • Human feedback protocol is explicit

    Detected: Pairwise Preference

  • Evaluation mode is explicit

    No clear evaluation mode extracted.

  • Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Metric reporting is present

    No metric terms extracted.