# SlangDIT: Benchmarking LLMs in Interpretative Slang Translation

arXiv: https://arxiv.org/abs/2505.14181 · HTML: https://mlsanity.com/paper/2505.14181/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 36 references that are in the map.
- First posted on arXiv 20 May 2025.

## Builds on (most cited first, 18 of 18)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models](https://mlsanity.com/paper/2403.13372/) (2024)
- [COMET: A Neural Framework for MT Evaluation](https://mlsanity.com/paper/2009.09025/) (2020)
- [Is ChatGPT a Good NLG Evaluator? A Preliminary Study](https://mlsanity.com/paper/2303.04048/) (2023)
- [Large Language Models Are State-of-the-Art Evaluators of Translation Quality](https://mlsanity.com/paper/2302.14520/) (2023)
- [CometKiwi: IST-Unbabel 2022 Submission for the Quality Estimation Shared Task](https://mlsanity.com/paper/2209.06243/) (2022)
- [Marco-o1: Towards Open Reasoning Models for Open-Ended Solutions](https://mlsanity.com/paper/2411.14405/) (2024)
- [O1 Replication Journey: A Strategic Progress Report -- Part 1](https://mlsanity.com/paper/2410.18982/) (2024)
- [O1 Replication Journey -- Part 2: Surpassing O1-preview through Simple Distillation, Big Progress or Bitter Lesson?](https://mlsanity.com/paper/2411.16489/) (2024)
- [o1-Coder: an o1 Replication for Coding](https://mlsanity.com/paper/2412.00154/) (2024)
- [Definition Modeling: Learning to define word embeddings in natural language](https://mlsanity.com/paper/1612.00394/) (2016)
- [Learning to Explain Non-Standard English Words and Phrases](https://mlsanity.com/paper/1709.09254/) (2017)
- [MSCTD: A Multimodal Sentiment Chat Translation Dataset](https://mlsanity.com/paper/2202.13645/) (2022)
- [Slangvolution: A Causal Analysis of Semantic Change and Frequency Dynamics in Slang](https://mlsanity.com/paper/2203.04651/) (2022)
- [Assisting Language Learners: Automated Trans-Lingual Definition Generation via Contrastive Prompt Learning](https://mlsanity.com/paper/2306.06058/) (2023)
- [Large Language Models for Classical Chinese Poetry Translation: Benchmarking, Evaluating, and Improving](https://mlsanity.com/paper/2408.09945/) (2024)
- [CLIX: Cross-Lingual Explanations of Idiomatic Expressions](https://mlsanity.com/paper/2501.03191/) (2025)

## Built on by (most cited first, 3 of 3)

- [Beyond Literal Translation: Evaluating Cultural Effectiveness in Social Media UGC](https://mlsanity.com/paper/2605.25626/) (2026)
- [ReflectMT: Internalizing Reflection for Efficient and High-Quality Machine Translation](https://mlsanity.com/paper/2604.19144/) (2026)
- [CIBuzzBench: A Benchmark for Cross-Lingual Understanding of Chinese Internet Buzzwords](https://mlsanity.com/paper/2609.21722/) (2026)

## Abstract (arXiv)

The challenge of slang translation lies in capturing context-dependent semantic extensions, as slang terms often convey meanings beyond their literal interpretation. While slang detection, explanation, and translation have been studied as isolated tasks in the era of large language models (LLMs), their intrinsic interdependence remains underexplored. The main reason is lacking of a benchmark where the two tasks can be a prerequisite for the third one, which can facilitate idiomatic translation. In this paper, we introduce the interpretative slang translation task (named SlangDIT) consisting of three sub-tasks: slang detection, cross-lingual slang explanation, and slang translation within the current context, aiming to generate more accurate translation with the help of slang detection and slang explanation. To this end, we construct a SlangDIT dataset, containing over 25k English-Chinese sentence pairs. Each source sentence mentions at least one slang term and is labeled with corresponding cross-lingual slang explanation. Based on the benchmark, we propose a deep thinking model, named SlangOWL. It firstly identifies whether the sentence contains a slang, and then judges whether the slang is polysemous and analyze its possible meaning. Further, the SlangOWL provides the best explanation of the slang term targeting on the current context. Finally, according to the whole thought, the SlangOWL offers a suitable translation. Our experiments on LLMs (e.g., Qwen2.5 and LLama-3.1), show that our deep thinking approach indeed enhances the performance of LLMs where the proposed SLangOWL significantly surpasses the vanilla models and supervised fine-tuned models without thinking.