Papers by Liwei Deng

2 papers
History Semantic Graph Enhanced Conversational KBQA with Temporal Information Modeling (2023.acl-long)

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Challenge: Existing methods for conversational KBQA assume the independence of utterances and model them in isolation.
Approach: They propose a History Semantic Graph Enhanced KBQA model that models long-range semantic dependencies in conversation history while maintaining low computational cost.
Outcome: The proposed model outperforms baselines on a widely used question type dataset.
Following the Autoregressive Nature of LLM Embeddings via Compression and Alignment (2025.emnlp-main)

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Challenge: Experimental results demonstrate that our method significantly outperforms traditional contrastive learning approaches when using the same amount of data.
Approach: They propose a new contrastive learning method built on embedding conditional probability distributions that integrates two tasks: information compression and conditional distribution alignment.
Outcome: The proposed method outperforms traditional contrastive learning approaches and achieves comparable performance to state-of-the-art models when using the same amount of data.

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