Papers by Zhiwei Gao
WSDMS: Debunk Fake News via Weakly Supervised Detection of Misinforming Sentences with Contextualized Social Wisdom (2023.emnlp-main)
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| Challenge: | Existing methods for debunking fake news rely on blending of authentic and fabricated content by creators. |
| Approach: | They propose a model that detects misinformation at sentence-level using social media conversations . they use a bag-level annotation system to train the model . |
| Outcome: | The proposed model outperforms existing state-of-the-art models on three real-world benchmarks and outperformed existing state of the art models in debunking fake news at sentence and article levels. |
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 . |
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. |
MarkLLM: An Open-Source Toolkit for LLM Watermarking (2024.emnlp-demo)
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Leyi Pan, Aiwei Liu, Zhiwei He, Zitian Gao, Xuandong Zhao, Yijian Lu, Binglin Zhou, Shuliang Liu, Xuming Hu, Lijie Wen, Irwin King, Philip Yu
| Challenge: | Large Language Models (LLMs) embed imperceptible yet algorithmically detectable signals in outputs to identify LLM-generated text. |
| Approach: | They propose to develop an open-source toolkit for LLM watermarking that embeds imperceptible yet algorithmically detectable signals in model outputs to identify LLM-generated text. |
| Outcome: | MarkLLM provides a unified framework for implementing LLM watermarking algorithms, while providing user-friendly interfaces to ensure ease of access. |
Improving Domain Generalization for Prompt-Aware Essay Scoring via Disentangled Representation Learning (2023.acl-long)
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| Challenge: | Existing AES models are either prompt-specific or prompt-adaptive and cannot generalize well on โunseenโ prompts. |
| Approach: | They propose a prompt-aware neural AES model to extract comprehensive representation for essay scoring, including both prompt-invariant and prompt-specific features. |
| Outcome: | The proposed model extracts comprehensive representation for essay scoring, including both prompt-invariant and prompt-specific features. |
Offensive Language Detection on Video Live Streaming Chat (2020.coling-main)
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| Challenge: | a prototype of a live chat room that detects offensive expressions in live streaming chats is presented . offensive expression detection on social media platforms can provide more protection for users . |
| Approach: | They propose a live chat room that detects offensive expressions in live streaming chats in real time . they used a dataset from Twitch to analyze offensive expression patterns . |
| Outcome: | The proposed chat room detects offensive expressions in live streaming chats in real time. |