Papers by Xingxing Wang
Two Pathways to Truthfulness: On the Intrinsic Encoding of LLM Hallucinations (2026.acl-long)
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Wen Luo, Guangyue Peng, Wei Li, Shaohang Wei, Feifan Song, Liang Wang, Nan Yang, Xingxing Zhang, Jing Jin, Furu Wei, Houfeng Wang
| Challenge: | Previous work shows that large language models generate hallucinations, yet the origins and mechanisms of these signals remain unclear. |
| Approach: | They propose to validate and disentangle two different pathways for truthfulness cues . they also propose to use the same mechanism to derive self-contained evidence from the generated answer . |
| Outcome: | The proposed applications improve hallucination detection performance by integrating two different inputs. |
Neural Label Search for Zero-Shot Multi-Lingual Extractive Summarization (2022.acl-long)
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| Challenge: | Existing methods to translate sentences to other languages using heuristics are challenging. |
| Approach: | They propose a model that learns hierarchical weights for different sets of labels and applies them to other languages to translate them. |
| Outcome: | The proposed model can translate English datasets to other languages and obtain different sets of labels again using heuristics. |
Towards Preference Following in Tool Calling Language Agents (2026.findings-acl)
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| Challenge: | Currently, large language model (LLM)-based agents can't follow user preferences when calling tools. |
| Approach: | They propose a benchmark to evaluate agents' ability to identify personalized user preferences from interaction histories and to adhere to these preferences when calling tools. |
| Outcome: | The proposed model achieves 51.16% accuracy on the APOLLO benchmark, while GPT-4o achieves only 51.13% accuracy. |
Understanding and Mitigating Spurious Signal Amplification in Test-Time Reinforcement Learning for Math Reasoning (2026.findings-acl)
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| Challenge: | a framework to mitigate spurious optimization signals is proposed for test-time reinforcement learning (TTRL) Reinforcement learning with verifiable rewards (RLVR) is an effective paradigm for improving large language models on structured challenging reasoning tasks. |
| Approach: | They propose a framework to mitigate spurious optimization signals from label noise . they propose to use a frequency-based sampling strategy to exclude ambiguous samples . |
| Outcome: | The proposed framework outperforms existing TTRL baselines on three large language models across multiple mathematical reasoning benchmarks. |
Chain-of-Reasoning: Towards Unified Mathematical Reasoning in Large Language Models via a Multi-Paradigm Perspective (2025.acl-long)
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Yiyao Yu, Yuxiang Zhang, Dongdong Zhang, Xiao Liang, Hengyuan Zhang, Xingxing Zhang, Mahmoud Khademi, Hany Hassan Awadalla, Junjie Wang, Yujiu Yang, Furu Wei
| Challenge: | Existing work shows that LLMs rely on single-paradigm reasoning that limits their effectiveness across diverse tasks. |
| Approach: | They propose a new framework that integrates multiple reasoning paradigms to enable synergistic collaboration. |
| Outcome: | The proposed model outperforms current SOTA models in theorem proving tasks and the MATH benchmark in arithmetic tasks. |