Papers by Shining Liang
Selected Languages are All You Need for Cross-lingual Truthfulness Transfer (2025.coling-main)
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| Challenge: | Existing methods for truthfulness enhancement in English are limited to multilingual scenarios. |
| Approach: | They propose a method for cross-lingual truthfulness transfer that uses language bias and transfer contributions to select an optimal subset of all tested languages and employ translation instruction tuning for cross language truthfulness transfers. |
| Outcome: | The proposed method reduces multilingual representation disparity and boosts cross-lingual truthfulness transfer of LLMs. |
Quantifying and Improving the Robustness of Retrieval-Augmented Language Models Against Spurious Features in Grounding Data (2026.acl-long)
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Shiping Yang, Jie Wu, Wenbiao Ding, Ning Wu, Shining Liang, Ming Gong, Hongzhi Li, Hengyuan Zhang, Angel X. Chang, Dongmei Zhang
| Challenge: | Existing studies on robustness to explicit noise (e.g., document semantics) but overlook implicit noise (spurious features). |
| Approach: | They propose a framework to quantify the robustness of RAGs against spurious features by integrating a data synthesis pipeline and a taxonomy. |
| Outcome: | The proposed framework quantifies the robustness of RALMs against spurious features. |
A Review of Dataset and Labeling Methods for Causality Extraction (2020.coling-main)
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| Challenge: | Existing methods for causal relationship extraction are limited and lack of unified methods hinder progress in the field. |
| Approach: | They propose to summarize existing methods and propose a new causal sequence label method . they propose to use multiple candidate causal label sequences according to label controversy . |
| Outcome: | The proposed method summarises existing methods and explores their practicability and extensibility from multiple perspectives. |
Beyond Rejection Sampling: Trajectory Fusion for Scaling Mathematical Reasoning (2026.findings-acl)
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| Challenge: | Large language models (LLMs) fine-tuned using rejection sampling retain only correct reasoning trajectories . however, this paradigm treats supervision as a binary filter that systematically excludes teacher-generated errors, leaving a gap in how reasoning failures are modeled during training. |
| Approach: | They propose a fine-tuning strategy that reframes rejection sampling as a structured supervision construction process. |
| Outcome: | The proposed approach outperforms RFT on multiple math benchmarks while retaining only correct reasoning trajectories. |
Label-aware Multi-level Contrastive Learning for Cross-lingual Spoken Language Understanding (2022.emnlp-main)
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| Challenge: | Existing approaches to translate spoken language understanding into low-resource languages are limited to implicit alignment and disregard the inherent semantic structure in SLU. |
| Approach: | They propose to model utterance-slot-word structure by a multi-level contrastive learning framework . they also propose a label-aware joint model leveraging label semantics to enhance alignment . |
| Outcome: | The proposed model improves performance on two zero-shot cross-lingual datasets. |