# Reheat Nachos for Dinner? Evaluating AI Support for Cross-Cultural Communication of Neologisms

arXiv: https://arxiv.org/abs/2604.23842 · HTML: https://mlsanity.com/paper/2604.23842/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 73 references that are in the map.
- First posted on arXiv 26 Apr 2026.

## Builds on (most cited first, 16 of 16)

- [Language Models (Mostly) Know What They Know](https://mlsanity.com/paper/2207.05221/) (2022)
- [Teaching Models to Express Their Uncertainty in Words](https://mlsanity.com/paper/2205.14334/) (2022)
- [Clio: Privacy-Preserving Insights into Real-World AI Use](https://mlsanity.com/paper/2412.13678/) (2024)
- [Rethinking Model Evaluation as Narrowing the Socio-Technical Gap](https://mlsanity.com/paper/2306.03100/) (2023)
- [From Dogwhistles to Bullhorns: Unveiling Coded Rhetoric with Language Models](https://mlsanity.com/paper/2305.17174/) (2023)
- [NEO-BENCH: Evaluating Robustness of Large Language Models with Neologisms](https://mlsanity.com/paper/2402.12261/) (2024)
- [From Text to Self: Users' Perceptions of Potential of AI on Interpersonal Communication and Self](https://mlsanity.com/paper/2310.03976/) (2023)
- [NYTWIT: A Dataset of Novel Words in the New York Times](https://mlsanity.com/paper/2003.03444/) (2020)
- [NewTerm: Benchmarking Real-Time New Terms for Large Language Models with Annual Updates](https://mlsanity.com/paper/2410.20814/) (2024)
- [An Interdisciplinary Approach to Human-Centered Machine Translation](https://mlsanity.com/paper/2506.13468/) (2025)
- [Should I Share this Translation? Evaluating Quality Feedback for User Reliance on Machine Translation](https://mlsanity.com/paper/2505.24683/) (2025)
- [Conversational AI as a Catalyst for Informal Learning: An Empirical Large-Scale Study on LLM Use in Everyday Learning](https://mlsanity.com/paper/2506.11789/) (2025)
- [Design Opportunities for Explainable AI Paraphrasing Tools: A User Study with Non-native English Speakers](https://mlsanity.com/paper/2405.07475/) (2024)
- [Emo, Love, and God: Making Sense of Urban Dictionary, a Crowd-Sourced Online Dictionary](https://mlsanity.com/paper/1712.08647/) (2017)
- [NeoN: A Tool for Automated Detection, Linguistic and LLM-Driven Analysis of Neologisms in Polish](https://mlsanity.com/paper/2505.15426/) (2025)
- [The News Says, the Bot Says: How Immigrants and Locals Differ in Chatbot-Facilitated News Reading](https://mlsanity.com/paper/2503.07797/) (2025)

## Built on by (most cited first, 1 of 1)

- [Last Translation Benchmark](https://mlsanity.com/paper/2609.04173/) (2026)

## Abstract (arXiv)

Neologisms and emerging slang are central to daily conversation, yet challenging for non-native speakers (NNS) to interpret and use appropriately in cross-cultural communication with native speakers (NS). NNS increasingly make use of Artificial Intelligence (AI) tools to learn these words. We study the utility of such tools in mediating an informal communication scenario through a human-subjects study (N=234): NNS participants learn English neologisms with AI support, write messages using the learned word to an NS friend, and judge contextual appropriateness of the neologism in two provided writing samples. Using both NS evaluator-rated communicative competence of NNS-produced writing and NNS' contextual appropriateness judgments, we compare three AI-based support conditions: AI Definition, AI Rewrite into simpler English, AI Explanation of meaning and usage, and Non-AI Dictionary for comparison. We show that AI Explanation yields the largest gains over no support in NS-rated competence, while contextual appropriateness judgments show indifference across support. NNS participants' self-reported perceptions tend to overestimate NS ratings, revealing a mismatch between perceived and actual competence. We further observe a significant gap between NNS- and NS-produced writing, highlighting the limitations of current AI tools and informing design for future tools.