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HFEPX · Eval paper review

SLAyiNG: A Diverse and Community-validated Dataset of Queer Slang

Leonor Veloso, Lea Hirlimann, Lucija Mihić Zidar, Philipp Wicke +2 more

Published

Sep 22, 2025

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

Aug 13, 2026

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Critique Edit General

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

Queer vernacular is rarely studied in NLP, despite advancements in resources and evaluation for other sociolects and informal language. Because of this, NLP systems often process queer language incorrectly, e.g., they misclassify it as hate speech or generate negative responses. To address this problem, we propose Slaying, the first real-world dataset of English queer slang. Slaying is community-validated, and includes over 500 queer slang terms that pertain to more than 20 queer subcommunities. We argue that queer language data resources have great potential in NLP -- e.g., as components of large pretraining corpora and as the basis for benchmarks -- and can improve queer users' experience of NLP systems. We leverage Slaying for two novel findings in support of this argument: (i) For a number of language models, we show that they are unbiased towards the queer community, but at the same time unable to process its language, i.e., absence of representation bias does not entail the absence of linguistic bias. (ii) Model performance on queer slang varies across queer subcommunities; it is generally worse for slang pertaining to African-American and Latine communities. These findings are relevant for both the queer NLP and the broader ML communities. Slaying is available to the public, and open to future revisions and extensions. Warning: This paper contains profane and potentially offensive language.

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

Critique Edit

Directly usable for protocol triage.

"Queer vernacular is rarely studied in NLP, despite advancements in resources and evaluation for other sociolects and informal language."

Evaluation Modes

missing

None explicit

Validate eval design from full paper text.

"Queer vernacular is rarely studied in NLP, despite advancements in resources and evaluation for other sociolects and informal language."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Queer vernacular is rarely studied in NLP, despite advancements in resources and evaluation for other sociolects and informal language."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"Queer vernacular is rarely studied in NLP, despite advancements in resources and evaluation for other sociolects and informal language."

Reported Metrics

missing

Not extracted

No metric anchors detected.

"Queer vernacular is rarely studied in NLP, despite advancements in resources and evaluation for other sociolects and informal language."

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
Critique Edit
Rater population
Not reported
Expertise required
General
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

Queer vernacular is rarely studied in NLP, despite advancements in resources and evaluation for other sociolects and informal language.

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

Key takeaways

  • Queer vernacular is rarely studied in NLP, despite advancements in resources and evaluation for other sociolects and informal language.
  • Because of this, NLP systems often process queer language incorrectly, e.g., they misclassify it as hate speech or generate negative responses.
  • To address this problem, we propose Slaying, the first real-world dataset of English queer slang.

Researcher actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
  • 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

  • Queer vernacular is rarely studied in NLP, despite advancements in resources and evaluation for other sociolects and informal language.
  • To address this problem, we propose Slaying, the first real-world dataset of English queer slang.
  • We leverage Slaying for two novel findings in support of this argument: (i) For a number of language models, we show that they are unbiased towards the queer community, but at the same time unable to process its language, i.e., absence of…

Why it matters for eval

  • Queer vernacular is rarely studied in NLP, despite advancements in resources and evaluation for other sociolects and informal language.
  • We argue that queer language data resources have great potential in NLP -- e.g., as components of large pretraining corpora and as the basis for benchmarks -- and can improve queer users' experience of NLP systems.

Researcher checklist

  • Human feedback protocol is explicit

    Detected: Critique Edit

  • 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.