Papers by Jiangcun Du
FuxiTranyu: A Multilingual Large Language Model Trained with Balanced Data (2024.emnlp-industry)
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Haoran Sun, Renren Jin, Shaoyang Xu, Leiyu Pan, null Supryadi, Menglong Cui, Jiangcun Du, Yikun Lei, Lei Yang, Ling Shi, Juesi Xiao, Shaolin Zhu, Deyi Xiong
| Challenge: | Large language models exhibit significant performance discrepancies between high- and low-resource languages. |
| Approach: | They present an open-source multilingual LLM with 8 billion parameters and a multilingual instruction dataset. |
| Outcome: | The proposed model achieves consistent multilingual representations across languages. |
A Comprehensive Evaluation of Quantization Strategies for Large Language Models (2024.findings-acl)
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| Challenge: | Quantization studies have focused on instruction-tuned LLMs, leaving their performance on other benchmarks unclear. |
| Approach: | They propose a framework to evaluate quantized large language models using four dimensions . they propose to reduce the bits needed for model weights or activations with minimal performance loss . |
| Outcome: | The proposed framework can retain comparable performance to non-quantized LLMs on most benchmarks. |
Efficiently Exploring Large Language Models for Document-Level Machine Translation with In-context Learning (2024.findings-acl)
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| Challenge: | Existing studies on sentence-level translation have focused on document level machine translation (DOCMT) document level translation is a complex task different from sentence- level translation. |
| Approach: | They propose a Context-Aware Prompting method which generates more accurate, coherent translations via in-context learning. |
| Outcome: | The proposed method is effective in literary translation tasks and zero pronoun translation tasks. |