Papers by Simeng Wu

3 papers
Multilingual Generative Retrieval via Cross-lingual Semantic Compression (2025.findings-emnlp)

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Challenge: Existing methods for multilingual retrieval still face cross-lingual identifier misalignment and identifiere inflation.
Approach: They propose a framework that unifies semantically equivalent multilingual keywords into shared atoms to align semantics and compresses the identifier space.
Outcome: The proposed framework improves cross-lingual alignment and reduces redundancy.
Benchmarking Generation and Evaluation Capabilities of Large Language Models for Instruction Controllable Summarization (2024.findings-naacl)

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Challenge: Recent studies have found that large language models (LLMs) can achieve state-of-the-art performance on generic summarization benchmarks, but their performance on more complex summarizing task settings is less studied.
Approach: They benchmark large language models on instruction controllable text summarization . they use 4 evaluation protocols and 11 LLMs to evaluate their performance .
Outcome: The proposed model performs well on instruction controllable text summarization tasks with 4 evaluation protocols and 11 LLMs.
Revisiting the Gold Standard: Grounding Summarization Evaluation with Robust Human Evaluation (2023.acl-long)

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Challenge: Existing studies for summarization evaluation exhibit low inter-annotator agreement or lack scale.
Approach: They propose a modified summarization salience protocol based on fine-grained semantic units and a robust summarizing evaluation benchmark.
Outcome: The proposed protocol is based on fine-grained semantic units and allows for high inter-annotator agreement.

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