Zhuosheng Zhang
12 papers in the PaperMetrix corpus
Papers by this author
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Dialogue Graph Modeling for Conversational Machine Reading
2021
Conversational Machine Reading (CMR) aims at answering questions in complicated interactive scenarios. Machine needs to answer questions through interactions with users based on given rule document, user scenario and dialogue history, and even initiatively asks …
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Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese
2021 · arXiv (Cornell University)
Although pre-trained models (PLMs) have achieved remarkable improvements in a wide range of NLP tasks, they are expensive in terms of time and resources. This calls for the study of training more efficient models with …
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Self-Prompting Large Language Models for Zero-Shot Open-Domain QA
2024
Junlong Li, Jinyuan Wang, Zhuosheng Zhang, Hai Zhao. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.
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Improving Machine Translation with Human Feedback: An Exploration of Quality Estimation as a Reward Model
2024
Zhiwei He, Xing Wang, Wenxiang Jiao, Zhuosheng Zhang, Rui Wang, Shuming Shi, Zhaopeng Tu. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: …
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DocBench: A Benchmark for Evaluating LLM-based Document Reading Systems
2025
Anni Zou, Wenhao Yu, Hongming Zhang, Kaixin Ma, Deng Cai, Zhuosheng Zhang, Hai Zhao, Dong Yu. Proceedings of the 4th International Workshop on Knowledge-Augmented Methods for Natural Language Processing. 2025.
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How Deep is Love in LLMs' Hearts? Exploring Semantic Size in Human-like Cognition
2025 · arXiv (Cornell University)
How human cognitive abilities are formed has long captivated researchers. However, a significant challenge lies in developing meaningful methods to measure these complex processes. With the advent of large language models (LLMs), which now rival …
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Watch Out Your Album! On the Inadvertent Privacy Memorization in Multi-Modal Large Language Models
2025 · arXiv (Cornell University)
Multi-Modal Large Language Models (MLLMs) have exhibited remarkable performance on various vision-language tasks such as Visual Question Answering (VQA). Despite accumulating evidence of privacy concerns associated with task-relevant content, it remains unclear whether MLLMs inadvertently …
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Semantics-Aware BERT for Language Understanding
2020 · Proceedings of the AAAI Conference on Artificial Intelligence
The latest work on language representations carefully integrates contextualized features into language model training, which enables a series of success especially in various machine reading comprehension and natural language inference tasks. However, the existing language …
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SG-Net: Syntax-Guided Machine Reading Comprehension
2020 · Proceedings of the AAAI Conference on Artificial Intelligence
For machine reading comprehension, the capacity of effectively modeling the linguistic knowledge from the detail-riddled and lengthy passages and getting ride of the noises is essential to improve its performance. Traditional attentive models attend to …
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Retrospective Reader for Machine Reading Comprehension
2021 · Proceedings of the AAAI Conference on Artificial Intelligence
Machine reading comprehension (MRC) is an AI challenge that requires machines to determine the correct answers to questions based on a given passage. MRC systems must not only answer questions when necessary but also tactfully …
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Automatic Chain of Thought Prompting in Large Language Models
2022 · arXiv (Cornell University)
Large language models (LLMs) can perform complex reasoning by generating intermediate reasoning steps. Providing these steps for prompting demonstrations is called chain-of-thought (CoT) prompting. CoT prompting has two major paradigms. One leverages a simple prompt …
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Is ChatGPT a General-Purpose Natural Language Processing Task Solver?
2023
Spurred by advancements in scale, large language models (LLMs) have demonstrated the ability to perform a variety of natural language processing (NLP) tasks zero-shot—i.e., without adaptation on downstream data. Recently, the debut of ChatGPT has …