Skip to content
AI News HubLIVE
Original source2 min read

Cross-Lingual Persona Effects in LLM Negotiation

Summary

A study finds that persona instructions given to LLM negotiators (e.g., "headstrong") produce different negotiation behaviors depending on the language of interaction. Korean negotiations reached agreement more often and featured larger seller concessions than English ones, but the gap appeared only for neutral and headstrong buyers, not cooperative ones.

SourcearXiv Computational LinguisticsAuthor: Saehee Eom, Tejas Khandwekar
Cross-Lingual Persona Effects in LLM Negotiation
Report an error

The correction channel is not available yet. You can copy the article reference below for later.

Correction instructions
Read article

[Submitted on 30 Oct 2026]

Title:Cross-Lingual Persona Effects in LLM Negotiation

View a PDF of the paper titled Cross-Lingual Persona Effects in LLM Negotiation, by Saehee Eom and 1 other authors

View PDF HTML (experimental)

Abstract:Tell an LLM negotiator to be "headstrong" and it should hold firm on price, but does that instruction retain the same behavior in every language? To test this, we use identical buyer and seller instances of Gemini 3.6 Flash to negotiate the same 12 CraigslistBargain scenarios in English and Korean. The pilot-scale 2 x 2 x 3 design crosses interaction language, seller urgency (none or strong), and buyer persona (cooperative, neutral, or headstrong), yielding 432 attempted negotiations. We call this property cross-lingual social-policy fidelity: whether a prompt-defined social policy produces the same behavioral effect regardless of interaction language. It often does not. Korean negotiations reached agreement far more often than English (77.3% vs. 52.5%, a 24.8-point gap, Holm-adjusted p = .0039), but only for neutral and headstrong buyers; cooperative buyers were already near-ceiling in both languages. Buyers' opening offers barely differed by language; the gap instead emerged later, through larger seller concessions and a smaller final gap in Korean, and agreed prices were also lower in Korean. Repeating the full design with Qwen3.7 Flash as both agents gives directional support: agreement was again higher in Korean (+13.9 points vs. Gemini's +24.8, with an interval whose lower bound is near zero), and sellers again conceded more. Pragmatic analysis reveals why: while both languages frequently featured face-threatening acts like rejections and counter-offers, Korean dialogue softened these threats with substantially more politeness mitigation, especially among sellers (11.7% vs. 3.6%). These results show that such a policy can produce different simulated negotiation behavior, not just different wording, when only the interaction language changes.

Comments: Accepted to SocialAgent Workshop at NeurIPS 2026. 13 pages, 1 figure

Subjects:

Computation and Language (cs.CL)

Cite as: arXiv:2611.00300 [cs.CL]

(or arXiv:2611.00300v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2611.00300

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Saehee Eom [view email] [v1] Fri, 30 Oct 2026 19:23:04 UTC (98 KB)

Full-text links:

Access Paper:

View a PDF of the paper titled Cross-Lingual Persona Effects in LLM Negotiation, by Saehee Eom and 1 other authors

View PDF

HTML (experimental)

TeX Source

view license

Additional Features

Audio Summary

Current browse context:

cs.CL

new | recent | 2026-11

Change to browse by:

cs

References & Citations

SciX

Google Scholar

Semantic Scholar

Loading...

Data provided by:

Bibliographic Tools

Bibliographic and Citation Tools

Bibliographic Explorer Toggle

Bibliographic Explorer (What is the Explorer?)

Connected Papers Toggle

Connected Papers (What is Connected Papers?)

Litmaps Toggle

Litmaps (What is Litmaps?)

scite.ai Toggle

scite Smart Citations (What are Smart Citations?)

Code, Data, Media

Code, Data and Media Associated with this Article

alphaXiv Toggle

alphaXiv (What is alphaXiv?)

Links to Code Toggle

CatalyzeX Code Finder for Papers (What is CatalyzeX?)

DagsHub Toggle

DagsHub (What is DagsHub?)

GotitPub Toggle

Gotit.pub (What is GotitPub?)

Huggingface Toggle

Hugging Face (What is Huggingface?)

ScienceCast Toggle

ScienceCast (What is ScienceCast?)

Demos

Demos

Replicate Toggle

Replicate (What is Replicate?)

Spaces Toggle

Hugging Face Spaces (What is Spaces?)

Spaces Toggle

TXYZ.AI (What is TXYZ.AI?)

Related Papers

Recommenders and Search Tools

Link to Influence Flower

Influence Flower (What are Influence Flowers?)

Core recommender toggle

CORE Recommender (What is CORE?)

Author

Venue

Institution

Topic

About arXivLabs

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)

Key points and analysis

Article intelligence

EngineersAdvanced

Key points

  • Agreement rates were 77.3% in Korean versus 52.5% in English, a 24.8-point gap that was statistically significant and limited to neutral and headstrong buyers.
  • Korean dialogues contained substantially more politeness mitigation (11.7% vs. 3.6%), especially from sellers, which helped soften face-threatening acts.
  • A replication with Qwen3.7 Flash showed directionally consistent results, suggesting the effect is not model-specific.

Highlights and analysis are generated automatically and may contain errors. Check the original source.