no code implementations • ACL (dialdoc) 2021 • Jiapeng Li, Mingda Li, Longxuan Ma, Wei-Nan Zhang, Ting Liu
The task requires identifying the grounding knowledge in form of a document span for the next dialogue turn.
no code implementations • 5 Feb 2025 • Mouyang Cheng, Chu-Liang Fu, Ryotaro Okabe, Abhijatmedhi Chotrattanapituk, Artittaya Boonkird, Nguyen Tuan Hung, Mingda Li
Materials design is an important component of modern science and technology, yet traditional approaches rely heavily on trial-and-error and can be inefficient.
no code implementations • 14 Nov 2024 • Longxuan Ma, Mingda Li, Weinan Zhang, Jiapeng Li, Ting Liu
The retrieval models consist of Fusion, Matching, and Ranking modules, while the generative models comprise Dialogue and Knowledge Encoding, Knowledge Selection, and Response Generation modules.
1 code implementation • 28 Oct 2024 • Ryotaro Okabe, Zack West, Abhijatmedhi Chotrattanapituk, Mouyang Cheng, Denisse Córdova Carrizales, Weiwei Xie, Robert J. Cava, Mingda Li
Here, we present a framework using large language models (LLMs) to predict synthesis pathways for inorganic materials, including quantum materials.
no code implementations • 21 Oct 2024 • Longxuan Ma, Jiapeng Li, Mingda Li, Wei-Nan Zhang, Ting Liu
The framework consists of two modules: the Policy planner leverages policy-aware dialogue representation to select knowledge and predict the policy of the response; the generator uses policy/knowledge-aware dialogue representation for response generation.
1 code implementation • 8 Oct 2024 • Yongxin Guo, Jingyu Liu, Mingda Li, Xiaoying Tang, Qingbin Liu, Xi Chen
To effectively handle various tasks simultaneously and enable zero-shot prediction, there is a growing trend in employing video LLMs for VTG tasks.
1 code implementation • 1 Oct 2024 • Hongbo Wang, Mingda Li, Junyu Lu, Hebin Xia, Liang Yang, Bo Xu, Ruizhu Liu, Hongfei Lin
Disclaimer: Samples in this paper may be harmful and cause discomfort!
no code implementations • 10 Sep 2024 • Dingxin Cheng, Mingda Li, Jingyu Liu, Yongxin Guo, Bin Jiang, Qingbin Liu, Xi Chen, Bo Zhao
While this method excels in short video understanding, it may result in a blend of multiple event information in long videos due to coarse compression, which causes information redundancy.
no code implementations • 5 Sep 2024 • Mingze Gao, Jingyu Liu, Mingda Li, Jiangtao Xie, Qingbin Liu, Bo Zhao, Xi Chen, Hui Xiong
Multimodal Large Language Models (MLLMs) have significantly improved performance across various image-language applications.
no code implementations • 19 Aug 2024 • Mingda Li, Abhijit Mishra, Utkarsh Mujumdar
The use of Large Language Models (LLMs) for program code generation has gained substantial attention, but their biases and limitations with non-English prompts challenge global inclusivity.
no code implementations • 5 Jul 2024 • Ryotaro Okabe, Mouyang Cheng, Abhijatmedhi Chotrattanapituk, Nguyen Tuan Hung, Xiang Fu, Bowen Han, Yao Wang, Weiwei Xie, Robert J. Cava, Tommi S. Jaakkola, Yongqiang Cheng, Mingda Li
Since the properties of quantum materials are closely related to geometric patterns, our results indicate that SCIGEN provides a general framework for generating quantum materials candidates.
1 code implementation • 31 May 2024 • Mingda Li, Xinyu Li, Yifan Chen, Wenfeng Xuan, Weinan Zhang
Although Retrieval-Augmented Large Language Models (RALMs) demonstrate their superiority in terms of factuality, they do not consistently outperform the original retrieval-free Language Models (LMs).
1 code implementation • 22 May 2024 • Yongxin Guo, Jingyu Liu, Mingda Li, Dingxin Cheng, Xiaoying Tang, Dianbo Sui, Qingbin Liu, Xi Chen, Kevin Zhao
Video Temporal Grounding (VTG) strives to accurately pinpoint event timestamps in a specific video using linguistic queries, significantly impacting downstream tasks like video browsing and editing.
1 code implementation • 28 Dec 2023 • Abhijit Mishra, Mingda Li, Soham Deo
After adaptation, models are fine-tuned on encrypted versions of existing training datasets.
no code implementations • 15 Aug 2023 • Ziyu Zhuang, Qiguang Chen, Longxuan Ma, Mingda Li, Yi Han, Yushan Qian, Haopeng Bai, Zixian Feng, Weinan Zhang, Ting Liu
From pre-trained language model (PLM) to large language model (LLM), the field of natural language processing (NLP) has witnessed steep performance gains and wide practical uses.
no code implementations • 21 Feb 2023 • Jinglun Cai, Mingda Li, Ziyan Jiang, Eunah Cho, Zheng Chen, Yang Liu, Xing Fan, Chenlei Guo
Query Rewriting (QR) plays a critical role in large-scale dialogue systems for reducing frictions.
1 code implementation • 7 Feb 2023 • Ryotaro Okabe, Shangjie Xue, Jiankai Yu, Tongtong Liu, Benoit Forget, Stefanie Jegelka, Gordon Kohse, Lin-wen Hu, Mingda Li
Here we present a computational framework using Tetris-inspired detector pixels and machine learning for radiation mapping.
1 code implementation • COLING 2022 • Longxuan Ma, Ziyu Zhuang, Weinan Zhang, Mingda Li, Ting Liu
This paper introduces a novel Self-supervised Fine-grained Dialogue Evaluation framework (SelF-Eval).
no code implementations • 8 Apr 2022 • Zijun Xue, Ruirui Li, Mingda Li
Conversational artificial intelligence (AI) is becoming an increasingly popular topic among industry and academia.
no code implementations • 11 Jan 2021 • Thanh Nguyen, Yoichiro Tsurimaki, Ricardo Pablo-Pedro, Grigory Bednik, Anuj Apte, Nina Andrejevic, Mingda Li
Topological nodal semimetals are known to host a variety of fascinating electronic properties due to the topological protection of the band-touching nodes.
Materials Science
no code implementations • COLING 2020 • Mingda Li, Xinyue Liu, Weitong Ruan, Luca Soldaini, Wael Hamza, Chengwei Su
The comparison shows that our model could recover the transcription by integrating the fragmented information among hypotheses and identifying the frequent error patterns of the ASR module, and even rewrite the query for a better understanding, which reveals the characteristic of multi-task learning of broadcasting knowledge.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
no code implementations • 17 Apr 2020 • Longxuan Ma, Wei-Nan Zhang, Mingda Li, Ting Liu
We believe that extracting unstructured document(s) information is the future trend of the DS because a great amount of human knowledge lies in these document(s).
no code implementations • 11 Jan 2020 • Mingda Li, Weitong Ruan, Xinyue Liu, Luca Soldaini, Wael Hamza, Chengwei Su
The NLU module usually uses the first best interpretation of a given speech in downstream tasks such as domain and intent classification.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
no code implementations • 18 Sep 2019 • Ariyam Das, Youfu Li, Jin Wang, Mingda Li, Carlo Zaniolo
In the past, the semantic issues raised by the non-monotonic nature of aggregates often prevented their use in the recursive statements of logic programs and deductive databases.