Papers by Zhuoer Wang
FANTAstic SEquences and Where to Find Them: Faithful and Efficient API Call Generation through State-tracked Constrained Decoding and Reranking (2024.findings-emnlp)
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Zhuoer Wang, Leonardo Ribeiro, Alexandros Papangelis, Rohan Mukherjee, Tzu-Yen Wang, Xinyan Zhao, Arijit Biswas, James Caverlee, Angeliki Metallinou
| Challenge: | Existing supervised and in-context learning approaches suffer from high training costs, poor data efficiency, and generated API calls that can be unfaithful to the API documentation and the user’s request. |
| Approach: | They propose an output-side optimization approach that uses state-tracked constrained decoding and a lightweight model as the discriminator to rerank the beam-searched candidate generations of the large language model. |
| Outcome: | The proposed approach outperforms existing supervised and in-context learning approaches in the DSTC8 and API Bank datasets in terms of call generation accuracy, inference efficiency, and context efficiency. |
Faithful Low-Resource Data-to-Text Generation through Cycle Training (2023.acl-long)
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| Challenge: | Methods to generate text from structured data have advanced significantly in recent years, but can fail to produce output faithful to the input data, especially on out-of-domain data. |
| Approach: | They evaluate the effectiveness of cycle training by using two models which are inverses of each other to generate text from structured data and one which generates the structured data from natural language text. |
| Outcome: | The proposed approach achieves nearly the same performance as fully supervised approaches on the WebNLG, E2E, WTQ, and WSQL datasets. |
PARADE: A New Dataset for Paraphrase Identification Requiring Computer Science Domain Knowledge (2020.emnlp-main)
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| Challenge: | Paraphrase identification requires specialized domain knowledge to perform . state-of-the-art neural models and non-expert human annotators have poor performance on PARADE . |
| Approach: | They propose a benchmark dataset called PARADE for paraphrase identification that requires specialized domain knowledge. |
| Outcome: | The proposed dataset shows state-of-the-art models and non-expert human annotators have poor performance on PARADE. |
Unsupervised Candidate Answer Extraction through Differentiable Masker-Reconstructor Model (2023.findings-emnlp)
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| Challenge: | Existing methods for candidate answer extraction are reliant on linguistic rules or annotated data and face partial annotation issue and challenges in generalization. |
| Approach: | They propose an unsupervised approach that leverages the inherent structure of context passages through a Differentiable Masker-Reconstructor (DMR) Model with the enforcement of self-consistency for picking up salient information tokens. |
| Outcome: | The proposed model outperforms supervised and unsupervised methods in two datasets with exhaustively-annotated answers and shows that it is comparable to supervised methods. |
DHP Benchmark: Are LLMs Good NLG Evaluators? (2025.findings-naacl)
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Yicheng Wang, Jiayi Yuan, Yu-Neng Chuang, Zhuoer Wang, Yingchi Liu, Mark Cusick, Param Kulkarni, Zhengping Ji, Yasser Ibrahim, Xia Hu
| Challenge: | Large Language Models (LLMs) are increasingly serving as evaluators in Natural Language Generation (NLG) tasks. |
| Approach: | They propose a framework that measures the discernment of Large Language Models (LLMs) across diverse NLG tasks. |
| Outcome: | The proposed framework provides quantitative discernment scores for LLMs across four NLG tasks. |
Co2PT: Mitigating Bias in Pre-trained Language Models through Counterfactual Contrastive Prompt Tuning (2023.findings-emnlp)
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| Challenge: | Pre-trained language models can encode unfair social biases from large pre-training corpora and even amplify biase in downstream applications. |
| Approach: | They propose a *debias-while-prompt tuning* method for mitigating biases via counterfactual contrastive prompt tuning on downstream tasks. |
| Outcome: | The proposed method can mitigate biases on three extrinsic bias benchmarks and adapt to existing debiased language models. |
WildFeedback: Aligning LLMs With In-situ User Interactions And Feedback (2026.acl-long)
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Taiwei Shi, Zhuoer Wang, Longqi Yang, Ying-Chun Lin, Zexue He, Mengting Wan, Pei Zhou, Sujay Kumar Jauhar, Sihao Chen, Shan Xia, Hongfei Zhang, Jieyu Zhao, Xiaofeng Xu, Xia Song, Jennifer Neville
| Challenge: | Traditional alignment methods rely on human annotations and are subjective and misalignment with real-world user preferences. |
| Approach: | They propose a framework that leverages in-situ user feedback during conversations with LLMs to create preference datasets automatically. |
| Outcome: | The proposed framework identifies and classifies user feedback to LLM responses between conversation turns and creates examples of preferred and dispreferred responses according to user preferences. |