WhoSaidIt: Human-LLM Collaborative Annotation for Text-Based Multilingual Speaker-Attribute Classification

David Smith; Jackie Lee; Lingyu Gao; Meghan Jemison; Will Monroe

arxiv: 2605.26070 · v1 · pith:2MLGVM52new · submitted 2026-05-25 · 💻 cs.CL

WhoSaidIt: Human-LLM Collaborative Annotation for Text-Based Multilingual Speaker-Attribute Classification

Lingyu Gao , Will Monroe , David Smith , Meghan Jemison , Jackie Lee This is my paper

Pith reviewed 2026-06-29 21:44 UTC · model grok-4.3

classification 💻 cs.CL

keywords speaker attributesmultilingual datasetshuman-LLM collaborationannotation frameworktext classificationcross-lingual differencesspeaker identificationre-annotation

0 comments

The pith

A human-LLM collaboration framework stabilizes noisy multilingual speaker-attribute labels by targeting annotation disagreements.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces a method where large language models work with human experts to refine speaker attribute labels like gender or age from text in multiple languages. Starting with inconsistent initial annotations, the approach uses LLMs to highlight common reasoning patterns and focuses re-annotation on cases where labels disagree. This produces the WhoSaidIt dataset covering nine attributes and shows that label choices vary significantly across languages while LLMs perform unevenly on the task. Readers care because better labels improve training data for models that must interpret social cues in diverse linguistic contexts without massive new annotation budgets.

Core claim

The authors establish that iterative interaction between LLMs and experts surfaces recurring annotation rationales from a noisy corpus, and disagreement-focused sampling enables targeted re-annotation to create more stable multilingual labels for nine speaker attributes, with measurable divergence from original annotations and mixed LLM classification results.

What carries the argument

The human-LLM collaborative re-annotation framework that surfaces recurring rationales through iterative expert interaction and uses disagreement sampling for re-annotation.

If this is right

Original and revised annotations diverge substantially, particularly across languages.
LLMs exhibit both strengths and limitations when classifying speaker attributes from text.
Providing explicit rationales influences how models behave on the task.
The framework operates effectively under practical resource constraints for dataset construction.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Similar collaborative methods could apply to annotating other ambiguous text features like sentiment or intent in multilingual data.
Stabilized labels might lead to more reliable cross-lingual transfer in downstream speaker-attribute models.
The observed cross-lingual differences suggest that language-specific cultural factors play a larger role than previously quantified.
Extending the framework to additional languages could test its scalability beyond the nine attributes studied.

Load-bearing premise

Iterative LLM-expert interaction reliably identifies annotation rationales that experts can use to create more consistent labels across languages.

What would settle it

A controlled experiment showing that independent expert re-annotation without LLM assistance achieves equivalent stability and cross-lingual consistency as the collaborative method.

Figures

Figures reproduced from arXiv: 2605.26070 by David Smith, Jackie Lee, Lingyu Gao, Meghan Jemison, Will Monroe.

read the original abstract

Annotating speaker attributes from text is inherently ambiguous, particularly in multilingual settings where demographic and social cues are implicit and culturally variable. We propose a human-large language model (LLM) collaborative re-annotation framework for stabilizing multilingual speaker-attribute labels under practical resource constraints. Starting from a noisy corpus, we use LLMs to surface recurring annotation rationales through iterative interaction with experts, and apply disagreement-focused sampling for targeted re-annotation. Using this framework, we construct WhoSaidIt, a multilingual dataset covering nine speaker-attribute labels. We quantify divergence between original and revised annotations, benchmark recent LLMs, and analyze the effect of explicit rationales on model behavior. Our results reveal substantial cross-lingual differences in annotation decisions and demonstrate both the strengths and limitations of LLMs in speaker-attribute classification.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper gives a practical human-LLM annotation pipeline and releases the WhoSaidIt dataset, but measures divergence without testing whether the revised labels are actually more stable.

read the letter

The main takeaway is that this work describes an iterative human-LLM re-annotation loop for multilingual speaker attributes and ships a new dataset with nine labels. They use LLMs to pull out recurring rationales, sample on disagreements, and re-annotate from a noisy starting corpus.

What the paper does well is focus on a real constraint: limited expert time for ambiguous, culturally variable labels. The cross-lingual divergence numbers are concrete and show that annotation decisions differ across languages in ways that matter. Benchmarking recent LLMs on the task and checking how explicit rationales change model outputs adds some useful data points. Releasing the dataset is the clearest contribution here.

The soft spot is the missing link on stabilization. The central claim is that the process produces more stable labels, yet the results only report how much the new annotations diverge from the old ones. There is no before-and-after inter-annotator agreement, no test-retest check, and no control condition without the LLM step. Changes could come from LLM influence or simple drift rather than genuine improvement. That gap makes it hard to judge whether the framework delivers on its main promise.

This is for researchers who build or clean datasets for speaker-attribute classification and need practical ideas under resource limits. A reader working on annotation pipelines or multilingual data quality would find the dataset and the divergence analysis worth looking at.

It deserves a serious referee. The dataset and the pipeline are tangible enough to review, even if the evaluation needs the stability comparison added.

Referee Report

2 major / 1 minor

Summary. The manuscript proposes a human-LLM collaborative re-annotation framework for stabilizing multilingual speaker-attribute labels under resource constraints. It uses LLMs to surface recurring rationales via iterative expert interaction and disagreement-focused sampling for targeted re-annotation on a noisy corpus, resulting in the WhoSaidIt dataset with nine speaker-attribute labels. The work quantifies divergence between original and revised annotations, benchmarks recent LLMs, and analyzes the effect of explicit rationales on model behavior, revealing substantial cross-lingual differences in annotation decisions along with LLM strengths and limitations in speaker-attribute classification.

Significance. If the central claim holds, the framework would provide a practical method for improving label stability in multilingual annotation tasks with limited resources and offer useful benchmarks on LLM performance in this domain. The emphasis on rationale surfacing and cross-lingual analysis could inform future annotation pipelines. However, the absence of direct stability validation limits the assessed impact.

major comments (2)

[Abstract] Abstract: The claim that the framework 'stabilizes' multilingual speaker-attribute labels lacks supporting evidence. Results quantify divergence between original and revised annotations and cross-lingual differences but supply no before/after stability metric (e.g., IAA, test-retest agreement) or control condition without LLM input to demonstrate that changes reflect genuine stabilization rather than annotator drift or LLM influence.
[Abstract] Abstract: No quantitative evidence, error analysis, or validation steps are described to support claims about divergence and LLM behavior, preventing evaluation of whether iterative LLM-expert interaction reliably surfaces rationales that improve label stability across languages.

minor comments (1)

The abstract refers to 'nine speaker-attribute labels' without naming them; this should be specified in the introduction or methods for clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback. We provide point-by-point responses to the major comments below.

read point-by-point responses

Referee: [Abstract] Abstract: The claim that the framework 'stabilizes' multilingual speaker-attribute labels lacks supporting evidence. Results quantify divergence between original and revised annotations and cross-lingual differences but supply no before/after stability metric (e.g., IAA, test-retest agreement) or control condition without LLM input to demonstrate that changes reflect genuine stabilization rather than annotator drift or LLM influence.

Authors: We acknowledge that the manuscript does not include a direct before/after stability metric such as IAA or a control condition without LLM input. The reported divergence between original and revised annotations serves as an indirect indicator of the framework's effect via disagreement-focused sampling and rationale surfacing, but it does not formally demonstrate stabilization independent of annotator drift. We will revise the abstract to avoid the term 'stabilizes' and add an explicit limitations discussion addressing this point. revision: yes
Referee: [Abstract] Abstract: No quantitative evidence, error analysis, or validation steps are described to support claims about divergence and LLM behavior, preventing evaluation of whether iterative LLM-expert interaction reliably surfaces rationales that improve label stability across languages.

Authors: The manuscript reports quantitative divergence measures, LLM benchmarks, and rationale-effect analysis in the results sections. To strengthen support for the claims about the iterative process, we will expand the error analysis and validation details in the revision, with additional cross-lingual breakdowns. revision: partial

Circularity Check

0 steps flagged

No circularity; methodological paper with no derivations or self-referential reductions

full rationale

The paper describes an annotation framework, dataset construction via LLM-expert interaction, quantification of annotation divergence, and LLM benchmarking. No equations, fitted parameters presented as predictions, or load-bearing self-citations appear in the abstract or described content. Central claims rest on empirical process and results rather than any derivation that reduces to its own inputs by construction. This is the expected non-finding for a resource/construction paper without mathematical claims.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no equations, parameters, or explicit assumptions beyond the general claim that the framework stabilizes labels; therefore no free parameters, axioms, or invented entities can be identified.

pith-pipeline@v0.9.1-grok · 5668 in / 1149 out tokens · 36867 ms · 2026-06-29T21:44:37.273068+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

9 extracted references · 2 canonical work pages · 2 internal anchors

[1]

DiZiNER: Disagreement-guided Instruction Refinement via Pilot Annotation Simulation for Zero-shot Named Entity Recognition

Llms in the loop: Leveraging large language model annotations for active learning in low-resource languages. InMachine Learning and Knowledge Dis- covery in Databases. Applied Data Science Track - European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9-13, 2024, Proceedings, Part X, volume 14950 ofLecture Notes in Computer Sci- ence, pages 39...

work page internal anchor Pith review Pith/arXiv arXiv 2024
[2]

In Proceedings of the 2023 ACM Conference on Fair- ness, Accountability, and Transparency, FAccT ’23, page 1531–1552, New York, NY , USA

Auditing cross-cultural consistency of human- annotated labels for recommendation systems. In Proceedings of the 2023 ACM Conference on Fair- ness, Accountability, and Transparency, FAccT ’23, page 1531–1552, New York, NY , USA. Association for Computing Machinery. Francisco M. Rangel Pardo, Fabio Celli, Paolo Rosso, Martin Potthast, Benno Stein, and Walt...

2023
[3]

InWorking Notes of CLEF 2015 - Con- ference and Labs of the Evaluation forum, Toulouse, France, September 8-11, 2015, CEUR Workshop Pro- ceedings

Overview of the 3rd author profiling task at PAN 2015. InWorking Notes of CLEF 2015 - Con- ference and Labs of the Evaluation forum, Toulouse, France, September 8-11, 2015, CEUR Workshop Pro- ceedings. CEUR-WS.org. Barbara Plank. 2022. The “problem” of human label variation: On ground truth in data, modeling and evaluation. InProceedings of the 2022 Confe...

2015
[4]

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Topic or style? exploring the most useful features for authorship attribution. InProceedings of the 27th International Conference on Computational Linguistics, pages 343–353, Santa Fe, New Mexico, USA. Association for Computational Linguistics. Hope Schroeder, Deb Roy, and Jad Kabbara. 2025. Just put a human in the loop? investigating LLM-assisted annotat...

work page internal anchor Pith review Pith/arXiv arXiv 2025
[5]

They had sashimi yesterday

TwiSty: A multilingual Twitter stylometry cor- pus for gender and personality profiling. InProceed- ings of the Tenth International Conference on Lan- guage Resources and Evaluation (LREC’16), pages 1632–1637, Portorož, Slovenia. European Language Resources Association (ELRA). Xinru Wang, Hannah Kim, Sajjadur Rahman, Kushan Mitra, and Zhengjie Miao. 2024....

2024
[6]

I had sashimi yesterday

The speaker explicitly eats or expresses intent to eat meat, seafood, or eggs. E.g., “I had sashimi yesterday”
[7]

The main dish is steak

The sentence describes, recommends, buys, or cooks meat/egg dishes in a way that assumes the speaker or user is okay with meat. E.g., “The main dish is steak.”, “Do you like hot pots?”, “Let’s grill beef.”
[8]

I don’t eat raw fish

The sentence includes a negative statement that still implies meat-eating, e.g., “I don’t eat raw fish” which implies fish is okay when cooked. “ I don’t like boiled eggs” implies eggs are consumed otherwise
[9]

How much is the chicken?

The sentence contains general or descriptive references to meat or egg-based food that assumes it’s acceptable or desirable to the audience. E.g., “How much is the chicken?”, “Hot dogs are delicious.” Please assign a label of 0 only if: - The speaker clearly reject animal products consumption. E.g., “I’m vegetarian”, “I don’t eat eggs.” - There is no refe...

2026

[1] [1]

DiZiNER: Disagreement-guided Instruction Refinement via Pilot Annotation Simulation for Zero-shot Named Entity Recognition

Llms in the loop: Leveraging large language model annotations for active learning in low-resource languages. InMachine Learning and Knowledge Dis- covery in Databases. Applied Data Science Track - European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9-13, 2024, Proceedings, Part X, volume 14950 ofLecture Notes in Computer Sci- ence, pages 39...

work page internal anchor Pith review Pith/arXiv arXiv 2024

[2] [2]

In Proceedings of the 2023 ACM Conference on Fair- ness, Accountability, and Transparency, FAccT ’23, page 1531–1552, New York, NY , USA

Auditing cross-cultural consistency of human- annotated labels for recommendation systems. In Proceedings of the 2023 ACM Conference on Fair- ness, Accountability, and Transparency, FAccT ’23, page 1531–1552, New York, NY , USA. Association for Computing Machinery. Francisco M. Rangel Pardo, Fabio Celli, Paolo Rosso, Martin Potthast, Benno Stein, and Walt...

2023

[3] [3]

InWorking Notes of CLEF 2015 - Con- ference and Labs of the Evaluation forum, Toulouse, France, September 8-11, 2015, CEUR Workshop Pro- ceedings

Overview of the 3rd author profiling task at PAN 2015. InWorking Notes of CLEF 2015 - Con- ference and Labs of the Evaluation forum, Toulouse, France, September 8-11, 2015, CEUR Workshop Pro- ceedings. CEUR-WS.org. Barbara Plank. 2022. The “problem” of human label variation: On ground truth in data, modeling and evaluation. InProceedings of the 2022 Confe...

2015

[4] [4]

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Topic or style? exploring the most useful features for authorship attribution. InProceedings of the 27th International Conference on Computational Linguistics, pages 343–353, Santa Fe, New Mexico, USA. Association for Computational Linguistics. Hope Schroeder, Deb Roy, and Jad Kabbara. 2025. Just put a human in the loop? investigating LLM-assisted annotat...

work page internal anchor Pith review Pith/arXiv arXiv 2025

[5] [5]

They had sashimi yesterday

TwiSty: A multilingual Twitter stylometry cor- pus for gender and personality profiling. InProceed- ings of the Tenth International Conference on Lan- guage Resources and Evaluation (LREC’16), pages 1632–1637, Portorož, Slovenia. European Language Resources Association (ELRA). Xinru Wang, Hannah Kim, Sajjadur Rahman, Kushan Mitra, and Zhengjie Miao. 2024....

2024

[6] [6]

I had sashimi yesterday

The speaker explicitly eats or expresses intent to eat meat, seafood, or eggs. E.g., “I had sashimi yesterday”

[7] [7]

The main dish is steak

The sentence describes, recommends, buys, or cooks meat/egg dishes in a way that assumes the speaker or user is okay with meat. E.g., “The main dish is steak.”, “Do you like hot pots?”, “Let’s grill beef.”

[8] [8]

I don’t eat raw fish

The sentence includes a negative statement that still implies meat-eating, e.g., “I don’t eat raw fish” which implies fish is okay when cooked. “ I don’t like boiled eggs” implies eggs are consumed otherwise

[9] [9]

How much is the chicken?

The sentence contains general or descriptive references to meat or egg-based food that assumes it’s acceptable or desirable to the audience. E.g., “How much is the chicken?”, “Hot dogs are delicious.” Please assign a label of 0 only if: - The speaker clearly reject animal products consumption. E.g., “I’m vegetarian”, “I don’t eat eggs.” - There is no refe...

2026