Papers by Zengkui Sun
LCS: A Language Converter Strategy for Zero-Shot Neural Machine Translation (2024.findings-acl)
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| Challenge: | Existing LT strategies cannot indicate the desired target language on zero-shot translation, i.e., the off-target issue. |
| Approach: | They propose a language converter strategy that embeds the target language into the top encoder layers to mitigate confusion in the encoder and ensures stable language indication for the decoder. |
| Outcome: | The proposed language converter strategy significantly mitigates off-target issue on multiUN, TED, and OPUS-100 datasets. |
Cross-Lingual Knowledge Editing in Large Language Models (2024.acl-long)
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| Challenge: | Knowledge editing is a promising technique to adapt large language models to new knowledge without retraining from scratch. |
| Approach: | They propose to use a multilingual dataset to translate a large-scale cross-lingual synthetic dataset from English to Chinese and then to evaluate their performance in Chinese. |
| Outcome: | The proposed method can change model performance on several special cases without retraining from scratch. |
Outdated Issue Aware Decoding for Factual Knowledge Editing (2024.findings-acl)
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| Challenge: | Existing knowledge editing methods retain outdated responses for reasoning questions . naively retraining LLMs can be computationally intensive and can lead to catastrophic forgetting . |
| Approach: | They propose a simple yet effective decoding strategy to enhance edited models on reasoning questions. |
| Outcome: | The proposed method outDates ISsue aware deCOding (DISCO) to improve models on reasoning questions. |
An Empirical Study of Many-to-Many Summarization with Large Language Models (2025.acl-long)
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Jiaan Wang, Fandong Meng, Zengkui Sun, Yunlong Liang, Yuxuan Cao, Jiarong Xu, Haoxiang Shi, Jie Zhou
| Challenge: | Recent studies have shown that large language models (LLMs) have strong multilingual abilities, giving them the potential to perform M2MS in real applications. |
| Approach: | They propose to use many-to-many summarization (M2MS) to generate a brief summary in any language given a document also in any other language. |
| Outcome: | The proposed model outperforms zero-shot LLMs in terms of automatic evaluations. |
Dual-Space Knowledge Distillation for Large Language Models (2024.emnlp-main)
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| Challenge: | Existing large language models (LLMs) have strong generalization abilities due to their huge model capacities. |
| Approach: | They propose a dual-space knowledge distillation framework that unifies the output spaces of the two models for KD. |
| Outcome: | The proposed framework outperforms existing white-box KD frameworks on task-agnostic instruction-following benchmarks and can automatically align representations of two models with different vocabularies. |