Papers by Xia Cui
Multi-Source Attention for Unsupervised Domain Adaptation (2020.aacl-main)
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| Challenge: | Existing approaches for domain adaptation (UDA) focus on adapting to a domain from a single source domain, but labelled instances are not available for the target domain. |
| Approach: | They propose to model source-selection in unsupervised domain adaptation as an attention-learning problem, where attention is learned over the sources per given target instance. |
| Outcome: | The proposed method outperforms previous proposed methods on two cross-domain sentiment classification datasets and is able to explain the predictions. |
Please refuse to answer me! Mitigating Over-Refusal in Large Language Models via Adaptive Contrastive Decoding (2026.acl-long)
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| Challenge: | Existing methods for mitigating over-refusal can't maintain low refusal ratio for harmless queries while keeping high for malicious queries. |
| Approach: | They propose a model-agnostic approach to mitigate over-refusal in large language models . they propose an adaptive contrastive decoding strategy that incorporates or removes the refusal token distribution . |
| Outcome: | The proposed approach reduces the refusal ratio for over-refusal queries by 10.35% while increasing the refusal rate for malicious queries by 0.13%. |
Introducing Compiler Semantics into Large Language Models as Programming Language Translators: A Case Study of C to x86 Assembly (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) can be used to translate high-level programming languages to machine instructions. |
| Approach: | They propose two methods to solve a problem known as neural compilation by using a 13B model with a behavioral accuracy of over 91%. |
| Outcome: | The proposed approach outperforms the larger model by over 50% and achieves a behavioral accuracy of over 91% while outperforming the GPT-4 Turbo model. |