Papers by Jia Pan
Zero-Shot Cross-Lingual Transfer of Neural Machine Translation with Multilingual Pretrained Encoders (2021.emnlp-main)
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| Challenge: | Existing work on improving cross-lingual transferability of NMT model is under-explored. |
| Approach: | They propose a model that leverages a multilingual pretrained encoder to improve cross-lingual transferability. |
| Outcome: | The proposed model outperforms mBART and m2m-100 on a zero-shot cross-lingual transfer task. |
mCLIP: Multilingual CLIP via Cross-lingual Transfer (2023.acl-long)
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Guanhua Chen, Lu Hou, Yun Chen, Wenliang Dai, Lifeng Shang, Xin Jiang, Qun Liu, Jia Pan, Wenping Wang
| Challenge: | Existing multilingual vision-language pretrained models are biased towards English due to the lack of sufficient non-English image-text pairs. |
| Approach: | They propose to train a retrieval-efficient dual-stream multilingual VLP model by aligning CLIP model and a multilingual text encoder through a novel Triangle Cross-modal Knowledge Distillation method. |
| Outcome: | Empirical results show that mCLIP achieves new state-of-the-art performance for both zero-shot and finetuned multilingual image-text retrieval tasks. |
Universal Vulnerabilities in Large Language Models: Backdoor Attacks for In-context Learning (2024.emnlp-main)
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| Challenge: | In-context learning has shown high efficacy in several NLP tasks, especially in few-shot settings. |
| Approach: | They propose a backdoor attack method that poisons demonstration examples and poisons the demonstration context, preserving the model's generality. |
| Outcome: | The proposed method can make models behave in alignment with predefined intentions without fine-tuning the model. |
Not All Contexts Are Equal: Teaching LLMs Credibility-aware Generation (2024.emnlp-main)
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| Challenge: | Existing RAG paradigms suffer from the impact of flawed information introduced during the retrieval phrase, thereby diminishing the reliability and correctness of the generated output. |
| Approach: | They propose a framework that empowers models to discern and process information based on its credibility. |
| Outcome: | The proposed framework outperforms existing models with retrieval augmentation and exhibits robustness despite increasing noise in the context. |
TheoremLlama: Transforming General-Purpose LLMs into Lean4 Experts (2024.emnlp-main)
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| Challenge: | a framework for formal proof writing using formal languages like Lean4 is needed to prove mathematical theorems using formal language. |
| Approach: | They propose a framework that trains a general-purpose LLM to be a Lean4 expert. |
| Outcome: | The proposed framework achieves cumulative accuracies of 36.48% and 33.61% on MiniF2F-Valid and Test datasets. |
Towards Making the Most of Cross-Lingual Transfer for Zero-Shot Neural Machine Translation (2022.acl-long)
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| Challenge: | Existing unsupervised neural machine translation systems can degrade when labeled data is limited. |
| Approach: | They propose a multilingual pretraining and multilingual fine-tuning for facilitating cross-lingual transfer in zero-shot translation using a parallel dataset. |
| Outcome: | The proposed model outperforms state-of-the-art models on many-to-English translation by over 7.2 and 5.0 BLEU. |
Document Set Expansion with Positive-Unlabeled Learning Using Intractable Density Estimation (2024.lrec-main)
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| Challenge: | Existing approaches to Document Set Expansion (DSE) rely on the unrealistic assumption of knowing the class prior for positive samples in the collection. |
| Approach: | They propose a novel method that utilizes intractable density estimation models to learn the class prior for positive samples in the collection. |
| Outcome: | The proposed method is based on a set of examples from PubMed and Covid datasets in a transductive setting. |