Papers by Guoliang Xing
Improving the Robustness of Large Language Models via Consistency Alignment (2024.lrec-main)
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Yukun Zhao, Lingyong Yan, Weiwei Sun, Guoliang Xing, Shuaiqiang Wang, Chong Meng, Zhicong Cheng, Zhaochun Ren, Dawei Yin
| Challenge: | Large language models have shown tremendous success in following user instructions and generating helpful responses, but their robustness is still far from optimal. |
| Approach: | They propose a two-stage training framework that helps a model generalize on following instructions via similar instruction augmentations. |
| Outcome: | The proposed training framework improves diversity and aligns the model with human expectations by differentiating subtle differences in similar responses. |
Knowing What LLMs DO NOT Know: A Simple Yet Effective Self-Detection Method (2024.naacl-long)
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Yukun Zhao, Lingyong Yan, Weiwei Sun, Guoliang Xing, Chong Meng, Shuaiqiang Wang, Zhicong Cheng, Zhaochun Ren, Dawei Yin
| Challenge: | Recent literature reveals that Large Language Models (LLMs) hallucinate intermittently, which impedes their reliability for further utilization. |
| Approach: | They propose a self-detection method to detect which questions an LLM does not know by combining the two components to identify whether the model generates a non-factual response to the question. |
| Outcome: | The proposed method can detect which questions an LLM does not know across factoid question-answering, arithmetic reasoning, and commonsense reasoning tasks. |
VisLingInstruct: Elevating Zero-Shot Learning in Multi-Modal Language Models with Autonomous Instruction Optimization (2024.naacl-long)
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Dongsheng Zhu, Daniel Tang, Weidong Han, Jinghui Lu, Yukun Zhao, Guoliang Xing, Junfeng Wang, Dawei Yin
| Challenge: | Current MMLMs show impressive zero-shot abilities in multi-modal tasks, but their performance depends heavily on the quality of instructions. |
| Approach: | They propose a novel approach to advancing multi-modal language models in zero-shot learning by evaluating and optimizing instructional texts through In-Context Learning. |
| Outcome: | The proposed approach improves zero-shot performance in multi-modal tasks by evaluating and optimizing instructional texts. |