Papers by Xiaojun Bi

5 papers
Knowledge Verification to Nip Hallucination in the Bud (2024.emnlp-main)

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Challenge: Recent studies have shown that large language models generate responses that sound plausible but contradict factual knowledge, a phenomenon known as hallucination.
Approach: They propose a novel approach to align large language models to evaluate knowledge boundaries based on external knowledge to reduce hallucinations .
Outcome: The proposed approach reduces hallucinations across six benchmarks using foundation LLMs of varying backbones and scales.
Explore-Instruct: Enhancing Domain-Specific Instruction Coverage through Active Exploration (2023.emnlp-main)

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Challenge: Existing data for instruction-tuning are inadequate for a wide range of tasks, limiting the scope for nuanced comprehension and interactions within these domains.
Approach: They propose to use Large Language Models to explore a multitude of variations or possibilities to improve instruction-tuning data by active exploration.
Outcome: The proposed approach improves domain-specific instruction coverage and shows significant improvements over baselines.
Retrieval-Generation Alignment for End-to-End Task-Oriented Dialogue System (2023.emnlp-main)

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Challenge: generative models struggle to distinguish subtle differences among retrieved knowledge records, resulting in suboptimal quality of generated responses.
Approach: They propose to use maximum marginal likelihood to train a perceptive retriever by utilizing signals from response generation for supervision.
Outcome: The proposed approach improves on three task-oriented dialogue datasets using T5 and ChatGPT as the backbone models.
Multi-Grained Knowledge Retrieval for End-to-End Task-Oriented Dialog (2023.acl-long)

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Challenge: Existing systems blend knowledge retrieval with response generation and optimize them with direct supervision from reference responses.
Approach: They propose a multi-grained knowledge retrieval system that decouples knowledge retrievals from response generation and introduces an entity selector and an attribute selector to acquire multigrained information from the knowledge base.
Outcome: The proposed system performs better on small and large knowledge bases.
DongbaMIE: A Multimodal Information Extraction Dataset for Evaluating Semantic Understanding of Dongba Pictograms (2025.findings-emnlp)

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Challenge: Dongba pictographic is the only pictograph script still in use in the world.
Approach: DongbaMIE is the first dataset focusing on multimodal information extraction of Dongbe pictographs.
Outcome: The dataset contains 23,530 sentence-level and 2,539 paragraph-level high-quality text-image pairs.

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