# Context-Aware LLM Translation System Using Conversation Summarization and Dialogue History

arXiv: https://arxiv.org/abs/2410.16775 · HTML: https://mlsanity.com/paper/2410.16775/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 7 references that are in the map.
- First posted on arXiv 22 Oct 2024.

## Builds on (most cited first, 4 of 4)

- [Gemma 2: Improving Open Language Models at a Practical Size](https://mlsanity.com/paper/2408.00118/) (2024)
- [Keep Me Updated! Memory Management in Long-term Conversations](https://mlsanity.com/paper/2210.08750/) (2022)
- [When Does Translation Require Context? A Data-driven, Multilingual Exploration](https://mlsanity.com/paper/2109.07446/) (2021)
- [History-Aware Hierarchical Transformer for Multi-session Open-domain Dialogue System](https://mlsanity.com/paper/2302.00907/) (2023)

## Built on by (most cited first, 7 of 7)

- [Consistency of Responses and Continuations Generated by Large Language Models on Social Media](https://mlsanity.com/paper/2501.08102/) (2025)
- [Source-primed Multi-turn Conversation Helps Large Language Models Translate Documents](https://mlsanity.com/paper/2503.10494/) (2025)
- [edgeVLM: Cloud-edge Collaborative Real-time VLM based on Context Transfer](https://mlsanity.com/paper/2508.12638/) (2025)
- [A Context-aware Framework for Translation-mediated Conversations](https://mlsanity.com/paper/2412.04205/) (2024)
- [Can LLMs Simulate L2-English Dialogue? An Information-Theoretic Analysis of L1-Dependent Biases](https://mlsanity.com/paper/2502.14507/) (2025)
- [Beyond the Sentence: A Survey on Context-Aware Machine Translation with Large Language Models](https://mlsanity.com/paper/2506.07583/) (2025)
- [BaatCheet: A Multilingual Corpus for Dialogue Translation in Indian Languages](https://mlsanity.com/paper/2609.33296/) (2026)

## Abstract (arXiv)

Translating conversational text, particularly in customer support contexts, presents unique challenges due to its informal and unstructured nature. We propose a context-aware LLM translation system that leverages conversation summarization and dialogue history to enhance translation quality for the English-Korean language pair. Our approach incorporates the two most recent dialogues as raw data and a summary of earlier conversations to manage context length effectively. We demonstrate that this method significantly improves translation accuracy, maintaining coherence and consistency across conversations. This system offers a practical solution for customer support translation tasks, addressing the complexities of conversational text.