2 code implementations • 19 Feb 2024 • Xinbei Ma, Zhuosheng Zhang, Hai Zhao
We propose a Comprehensive Cognitive LLM Agent, CoCo-Agent, with two novel approaches, comprehensive environment perception (CEP) and conditional action prediction (CAP), to systematically improve the GUI automation performance.
1 code implementation • 8 Feb 2024 • Xinbei Ma, Tianjie Ju, Jiyang Qiu, Zhuosheng Zhang, Hai Zhao, Lifeng Liu, Yulong Wang
Q3: Which knowledge features are correlated with the performance and robustness of editing?
1 code implementation • 20 Nov 2023 • Zhuosheng Zhang, Yao Yao, Aston Zhang, Xiangru Tang, Xinbei Ma, Zhiwei He, Yiming Wang, Mark Gerstein, Rui Wang, Gongshen Liu, Hai Zhao
Large language models (LLMs) have dramatically enhanced the field of language intelligence, as demonstrably evidenced by their formidable empirical performance across a spectrum of complex reasoning tasks.
no code implementations • 18 Sep 2023 • Xinbei Ma, Yi Xu, Hai Zhao, Zhuosheng Zhang
On the other hand, the split segments are an appropriate element of multi-turn dialogue response selection.
no code implementations • 23 May 2023 • Xinbei Ma, Yeyun Gong, Pengcheng He, Hai Zhao, Nan Duan
Furthermore, to better align the query to the frozen modules, we propose a trainable scheme for our pipeline.
1 code implementation • 11 May 2023 • Xinbei Ma, Yeyun Gong, Pengcheng He, Hai Zhao, Nan Duan
Based on the remarkable achievements of pre-trained language models in abstractive summarization, the copying mechanism has proved helpful by improving the factuality, stability, and overall performance.
1 code implementation • ACL 2022 • Xinbei Ma, Zhuosheng Zhang, Hai Zhao
Tangled multi-party dialogue contexts lead to challenges for dialogue reading comprehension, where multiple dialogue threads flow simultaneously within a common dialogue record, increasing difficulties in understanding the dialogue history for both human and machine.
no code implementations • 9 Sep 2021 • Xinbei Ma, Zhuosheng Zhang, Hai Zhao
Multi-party multi-turn dialogue comprehension brings unprecedented challenges on handling the complicated scenarios from multiple speakers and criss-crossed discourse relationship among speaker-aware utterances.
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