301 citations · 428 across the 27 of their papers we have counts for
13 papers · 1 filter
HRoT: Hybrid prompt strategy and Retrieval of Thought for Table-Text Hybrid Question Answering
Tongxu Luo, Fangyu Lei, Jiahe Lei +4
Answering numerical questions over hybrid contents from the given tables and text(TextTableQA) is a challenging task. Recently, Large Language Models (LLMs) have gained significant…
MMHQA-ICL: Multimodal In-context Learning for Hybrid Question Answering over Text, Tables and Images
Weihao Liu, Fangyu Lei, Tongxu Luo +4
In the real world, knowledge often exists in a multimodal and heterogeneous form. Addressing the task of question answering with hybrid data types, including text, tables, and imag…
Soil Image Segmentation Based on Mask R-CNN
Yida Chen, Kang Liu, Yi Xin +1
The complex background in the soil image collected in the field natural environment will affect the subsequent soil image recognition based on machine vision. Segmenting the soil c…
Interpreting Sentiment Composition with Latent Semantic Tree
Zhongtao Jiang, Yuanzhe Zhang, Cao Liu +3
As the key to sentiment analysis, sentiment composition considers the classification of a constituent via classifications of its contained sub-constituents and rules operated on th…
Prompting Vision Language Model with Knowledge from Large Language Model for Knowledge-Based VQA
Yang Zhou, Pengfei Cao, Yubo Chen +2
Knowledge-based visual question answering is a very challenging and widely concerned task. Previous methods adopts the implicit knowledge in large language models (LLM) to achieve…
LMTuner: An user-friendly and highly-integrable Training Framework for fine-tuning Large Language Models
Yixuan Weng, Zhiqi Wang, Huanxuan Liao +4
With the burgeoning development in the realm of large language models (LLMs), the demand for efficient incremental training tailored to specific industries and domains continues to…
Towards Graph-hop Retrieval and Reasoning in Complex Question Answering over Textual Database
Minjun Zhu, Yixuan Weng, Shizhu He +2
In Textual question answering (TQA) systems, complex questions often require retrieving multiple textual fact chains with multiple reasoning steps. While existing benchmarks are li…
ParaLS: Lexical Substitution via Pretrained Paraphraser
Jipeng Qiang, Kang Liu, Yun Li +2
Lexical substitution (LS) aims at finding appropriate substitutes for a target word in a sentence. Recently, LS methods based on pretrained language models have made remarkable pro…
Large Language Models Need Holistically Thought in Medical Conversational QA
Yixuan Weng, Bin Li, Fei Xia +5
The medical conversational question answering (CQA) system aims at providing a series of professional medical services to improve the efficiency of medical care. Despite the succes…
Multi-View Graph Representation Learning for Answering Hybrid Numerical Reasoning Question
Yifan Wei, Fangyu Lei, Yuanzhe Zhang +2
Hybrid question answering (HybridQA) over the financial report contains both textual and tabular data, and requires the model to select the appropriate evidence for the numerical r…
SHQA: A Three-Stage Approach for Multi-hop Text-Table Hybrid Question Answering
Fangyu Lei, Xiang Li, Yifan Wei +4
Answering multi-hop questions over hybrid factual knowledge from the given text and table (TextTableQA) is a challenging task. Existing models mainly adopt a retriever-reader frame…
Mastering Symbolic Operations: Augmenting Language Models with Compiled Neural Networks
Yixuan Weng, Minjun Zhu, Fei Xia +4
Language models' (LMs) proficiency in handling deterministic symbolic reasoning and rule-based tasks remains limited due to their dependency implicit learning on textual data. To e…
Knowledge Reasoning via Jointly Modeling Knowledge Graphs and Soft Rules
Yinyu Lan, Shizhu He, Kang Liu +1
Knowledge graphs (KGs) play a crucial role in many applications, such as question answering, but incompleteness is an urgent issue for their broad application. Much research in kno…