# The Struggles of LLMs in Cross-lingual Code Clone Detection

arXiv: https://arxiv.org/abs/2408.04430 · HTML: https://mlsanity.com/paper/2408.04430/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 46 references that are in the map.
- First posted on arXiv 8 Aug 2024.

## Builds on (most cited first, 14 of 14)

- [StarCoder 2 and The Stack v2: The Next Generation](https://mlsanity.com/paper/2402.19173/) (2024)
- [UniXcoder: Unified Cross-Modal Pre-training for Code Representation](https://mlsanity.com/paper/2203.03850/) (2022)
- [CRUXEval: A Benchmark for Code Reasoning, Understanding and Execution](https://mlsanity.com/paper/2401.03065/) (2024)
- [CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks](https://mlsanity.com/paper/2105.12655/) (2021)
- [Detecting Code Clones with Graph Neural Networkand Flow-Augmented Abstract Syntax Tree](https://mlsanity.com/paper/2002.08653/) (2020)
- [Should We Respect LLMs? A Cross-Lingual Study on the Influence of Prompt Politeness on LLM Performance](https://mlsanity.com/paper/2402.14531/) (2024)
- [SourcererCC: Scaling Code Clone Detection to Big Code](https://mlsanity.com/paper/1512.06448/) (2015)
- [Oreo: Detection of Clones in the Twilight Zone](https://mlsanity.com/paper/1806.05837/) (2018)
- [Towards Understanding the Capability of Large Language Models on Code Clone Detection: A Survey](https://mlsanity.com/paper/2308.01191/) (2023)
- [Do Code Clones Matter?](https://mlsanity.com/paper/1701.05472/) (2017)
- [Investigating the Efficacy of Large Language Models for Code Clone Detection](https://mlsanity.com/paper/2401.13802/) (2024)
- [CCT-Code: Cross-Consistency Training for Multilingual Clone Detection and Code Search](https://mlsanity.com/paper/2305.11626/) (2023)
- [What You See is What it Means! Semantic Representation Learning of Code based on Visualization and Transfer Learning](https://mlsanity.com/paper/2002.02650/) (2020)
- [AdaCCD: Adaptive Semantic Contrasts Discovery Based Cross Lingual Adaptation for Code Clone Detection](https://mlsanity.com/paper/2311.07277/) (2023)

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

- [Evaluating Language Models on Cross-Language Code Functional Equivalence](https://mlsanity.com/paper/2608.23961/) (2026)
- [Code Review Without Borders: Evaluating Synthetic vs. Real Data for Review Recommendation](https://mlsanity.com/paper/2509.04810/) (2025)
- [Type-IV Code Clone Detection via Layer-Wise Non-Contrastive Representation Learning](https://mlsanity.com/paper/2609.17338/) (2026)

## Abstract (arXiv)

With the involvement of multiple programming languages in modern software development, cross-lingual code clone detection has gained traction within the software engineering community. Numerous studies have explored this topic, proposing various promising approaches. Inspired by the significant advances in machine learning in recent years, particularly Large Language Models (LLMs), which have demonstrated their ability to tackle various tasks, this paper revisits cross-lingual code clone detection. We evaluate the performance of five (05) LLMs and eight prompts (08) for the identification of cross-lingual code clones. Additionally, we compare these results against two baseline methods. Finally, we evaluate a pre-trained embedding model to assess the effectiveness of the generated representations for classifying clone and non-clone pairs. The studies involving LLMs and Embedding models are evaluated using two widely used cross-lingual datasets, XLCoST and CodeNet. Our results show that LLMs can achieve high F1 scores, up to 0.99, for straightforward programming examples. However, they not only perform less well on programs associated with complex programming challenges but also do not necessarily understand the meaning of "code clones" in a cross-lingual setting. We show that embedding models used to represent code fragments from different programming languages in the same representation space enable the training of a basic classifier that outperforms all LLMs by ~1 and ~20 percentage points on the XLCoST and CodeNet datasets, respectively. This finding suggests that, despite the apparent capabilities of LLMs, embeddings provided by embedding models offer suitable representations to achieve state-of-the-art performance in cross-lingual code clone detection.