Papers by Ziwei Sun

5 papers
Detecting Stealthy Backdoor Samples based on Intra-class Distance for Large Language Models (2025.findings-emnlp)

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Challenge: Existing detectors use classifier-style probability signals or rely on rewriting, which can degrade quality and introduce new triggers.
Approach: They propose to efficiently remove poisoned examples before or during fine-tuning .
Outcome: The proposed method outperforms prior detectors on two machine translation datasets and one QA dataset.
RAIDEN Benchmark: Evaluating Role-playing Conversational Agents with Measurement-Driven Custom Dialogues (2025.coling-main)

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Challenge: Existing benchmarks for RPCA evaluation are lacking for dialogues . authors introduce specialized judging LLM for automatic RPca evaluation . compelling role-playing agent is expected to lead to more in-depth conversations .
Approach: They propose a benchmark to assess the effectiveness of RPCA interactions using dialogues . they introduce a specialized judging LLM tailored for automatic RPca evaluation .
Outcome: The proposed benchmark focuses on assessing particular dimensions at different stages of a conversation, facilitated through interactions conducted by annotators.
CompTab: A Comprehensive Benchmark for Real-World TableQA with Complex Reasoning and Irregular Tables (2026.acl-long)

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Challenge: Existing benchmarks focus on well-structured tables and fail to reflect irregular structures and complex reasoning commonly encountered in real-world scenarios.
Approach: They propose a benchmark to evaluate TableQA under complex reasoning and irregular table conditions.
Outcome: The proposed framework improves generalization and realism of large language models under complex and irregular table conditions.
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.
MIRTH: Mutual-Information Reasoning with Temporal Hubs for Vision-Language-Action Agents (2026.acl-long)

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Challenge: a recent study shows that VLA models suffer from temporal myopia that discards historical dynamics and reasoning gaps between high-level instructions and low-level motor commands.
Approach: They propose a framework to address temporal myopia and autoregressive scalar decoding in VLAs . they propose two memory hubs that compress long-term scene evolution and short-term motion trends .
Outcome: The proposed framework achieves state-of-the-art performance and exhibiting emergent error recovery capabilities.

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