Papers by Zhiwei Cao
Robust Tool Use via Fission-GRPO: Learning to Recover from Execution Errors (2026.acl-long)
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Zhiwei Zhang, Fei Zhao, Rui Wang, Zezhong Wang, Bin Liang, Jiakang Wang, Yao Hu, Shaosheng Cao, Kam-Fai Wong
| Challenge: | Large language models (LLMs) can call tools effectively, but they remain brittle in multi-turn execution. |
| Approach: | They propose a framework that converts execution errors into on-policy corrective supervision within the RL training loop. |
| Outcome: | The proposed framework improves the error recovery rate of Qwen3-8B by 5.7% absolute and overall accuracy by 4.0% on BFCL v4 Multi-Turn. |
Retaining Key Information under High Compression Ratios: Query-Guided Compressor for LLMs (2024.acl-long)
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| Challenge: | Existing methods to compress long contexts have degraded dramatically as compression ratios increase, sometimes even falling to the closed-book level. |
| Approach: | They propose a query-guided compression method that preserves key information within the compressed context. |
| Outcome: | The proposed method can consistently perform well even at high compression ratios, and offers significant benefits in terms of inference cost and throughput. |
Same Claim, Different Judgment: Benchmarking Scenario-Induced Bias in Multilingual Financial Misinformation Detection (2026.findings-acl)
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Zhiwei Liu, Yupeng Cao, Yuechen Jiang, Mohsinul Kabir, Polydoros Giannouris, Chen Xu, Ziyang Xu, Tianlei Zhu, Md. Tariquzzaman, Triantafillos Papadopoulos, Yan Wang, Lingfei Qian, Xueqing Peng, Zhuohan Xie, Ye Yuan, Saeed Almheiri, Abdulrazzaq Alnajjar, Ming-Bin Chen, Harry Stuart, Paul Thompson, Prayag Tiwari, Alejandro Lopez-Lira, Xue Liu, Jimin Huang, Sophia Ananiadou
| Challenge: | Existing research on LLM biases has focused on direct questioning or general-purpose settings . pronounced behavioral biase despite their growing deployment in financial analysis, forecasting, and decision support. |
| Approach: | They propose a benchmark to evaluate behavioral biases of large language models in MFMD . they use a multilingual financial misinformation dataset to integrate these with misinformation claims . |
| Outcome: | The proposed benchmark evaluates behavioral biases of large language models across economic scenarios. |
Learn over Past, Evolve for Future: Forecasting Temporal Trends for Fake News Detection (2023.acl-industry)
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| Challenge: | Existing work on fake news detection does not consider the temporal shift issue caused by the rapidly-evolving nature of news data. |
| Approach: | They propose a framework to forecast temporal patterns of news data and guide detector to fast adapt to future distributions. |
| Outcome: | The proposed framework forecasts temporal distribution patterns and guides detector to fast adapt to future distribution. |
All That Glisters Is Not Gold: A Benchmark for Reference-Free Counterfactual Financial Misinformation Detection (2026.acl-long)
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Yuechen Jiang, Zhiwei Liu, Yupeng Cao, Yueru He, Ziyang Xu, Chen Xu, Zhiyang Deng, Prayag Tiwari, Xi Chen, Alejandro Lopez-Lira, Jimin Huang, Junichi Tsujii, Sophia Ananiadou
| Challenge: | RFC-Bench evaluates large language models on financial misinformation under realistic news . current models struggle to maintain coherent belief states without external grounding, study finds . |
| Approach: | They propose a benchmark for evaluating large language models on financial misinformation under realistic news. |
| Outcome: | The proposed model performs better when context is available, while reference-free settings expose significant weaknesses. |
MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application (2026.acl-long)
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Xueqing Peng, Lingfei Qian, Yan Wang, Ruoyu Xiang, Yueru He, Yang Ren, Mingyang Jiang, Vincent Jim Zhang, Yuqing Guo, Jeff Zhao, Huan He, Yi Han, Yun Feng, Yuechen Jiang, Yupeng Cao, Haohang Li, Yangyang Yu, Xiaoyu Wang, Penglei Gao, Shengyuan Lin, Keyi Wang, Shanshan Yang, Yilun Zhao, Zhiwei Liu, Peng Lu, Jerry Huang, Suyuchen Wang, Triantafillos Papadopoulos, Polydoros Giannouris, Efstathia Soufleri, Nuo Chen, Zhiyang Deng, Heming Fu, Yijia Zhao, Mingquan Lin, Meikang Qiu, Kaleb E Smith, Arman Cohan, Xiao-Yang Liu, Jimin Huang, Guojun Xiong, Alejandro Lopez-Lira, Xi Chen, Junichi Tsujii, Jian-Yun Nie, Sophia Ananiadou, Qianqian Xie
| Challenge: | Existing evaluations of LLMs in finance are text-only, monolingual, and largely saturated by current models. |
| Approach: | They propose a multilingual and multimodal benchmark for evaluating LLMs in real financial contexts. |
| Outcome: | The first expert-annotated multilingual and multimodal benchmark is released . it evaluates 21 leading LLMs and shows they perform better in multilingual settings . |
CHEER: Centrality-aware High-order Event Reasoning Network for Document-level Event Causality Identification (2023.acl-long)
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| Challenge: | Recent studies focus on building a document-level graph for cross-sentence reasoning, but ignore important causal structures. |
| Approach: | They propose a document-level event causality identification model which annotates central events and incorporates event centrality information into the reasoning network. |
| Outcome: | The proposed model performs high-order reasoning while considering event centrality. |
Bridging the Domain Gaps in Context Representations for k-Nearest Neighbor Neural Machine Translation (2023.acl-long)
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Zhiwei Cao, Baosong Yang, Huan Lin, Suhang Wu, Xiangpeng Wei, Dayiheng Liu, Jun Xie, Min Zhang, Jinsong Su
| Challenge: | Existing methods to improve k-Nearest neighbor machine translation (kNN-MT) are based on the ability to non-parametrically adapt to new domains. |
| Approach: | They propose a method to boost the datastore retrieval of k-Nearest neighbor machine translation by reconstructing the original datastore. |
| Outcome: | The proposed method boosts the retrieval and translation quality of k-Nearest neighbor machine translation by reconstructing the original datastore. |
Efficient k-Nearest-Neighbor Machine Translation with Dynamic Retrieval (2024.findings-acl)
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| Challenge: | Existing models for non-parametric domain adaptation lack kNN retrieval at each timestep, leading to substantial time overhead. |
| Approach: | They propose a kNN-MT-based model that uses a domain-specific translation knowledge store to interpolate the prediction distribution of the model. |
| Outcome: | The proposed model significantly extends kNN-MT with dynamic retrieval on widely-used datasets. |