Papers by Li Qiuxia
Tug-of-War between Knowledge: Exploring and Resolving Knowledge Conflicts in Retrieval-Augmented Language Models (2024.lrec-main)
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| Challenge: | Existing knowledge conflicts in RALMs can ensnare them in a tug-of-war between knowledge and evidence, limiting their practical applicability. |
| Approach: | They propose a method called Conflict-Disentangle Contrastive Decoding (CD2) to better calibrate the model’s confidence. |
| Outcome: | The proposed method can resolve knowledge conflicts in large language models with the help of conflict-disentangle contrast decoding (CD2) . |
Generating Temporally-ordered Event Sequences via Event Optimal Transport (2022.coling-1)
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| Challenge: | Existing methods for temporal event ordering and event infilling ignore the global semantics of events, and the model adopts a word-level objective to model events in texts. |
| Approach: | They propose a temporal event ordering and event infilling task using a model that uses maximum likelihood estimation to model events in texts. |
| Outcome: | The proposed model outperforms existing models on all evaluation datasets. |
Augmentation, Retrieval, Generation: Event Sequence Prediction with a Three-Stage Sequence-to-Sequence Approach (2022.coling-1)
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| Challenge: | Existing methods to predict event sequences are complex and ignore the knowledge of external events. |
| Approach: | They propose a statistical induction problem to generate a sequence of events by exploring the similarity between the given goal and known sequences of events. |
| Outcome: | The proposed model outperforms existing methods on an event sequence prediction task. |
Leros: Learning Explicit Reasoning on Synthesized Data for Commonsense Question Answering (2024.lrec-main)
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Chenhao Wang, Pengfei Cao, Jiachun Li, Yubo Chen, Kang Liu, Xiaojian Jiang, Jiexin Xu, Li Qiuxia, Jun Zhao
| Challenge: | Recent work shows large language models can generate useful rationales for commonsense question answering (CQA) however, the cost of deployment and further tuning is relatively expensive for the large models. |
| Approach: | They propose a framework that leverages both knowledge graphs and large language models to synthesize rationale-augmented CQA data. |
| Outcome: | The proposed model can generate useful rationales on unseen CQA benchmarks. |