activity
20162024
most citedLarge Language Models are Better Reasoners with Self-Verification

18 citations · 58 across the 21 of their papers we have counts for

collaborators
Showing cs.CLShow all

16 papers · 1 filter

cs.CL2024

Multilingual Knowledge Graph Completion from Pretrained Language Models with Knowledge Constraints

Ran Song, Shizhu He, Shengxiang Gao +4

Multilingual Knowledge Graph Completion (mKGC) aim at solving queries like (h, r, ?) in different languages by reasoning a tail entity t thus improving multilingual knowledge graph…

cs.CL2024

Find Parent then Label Children: A Two-stage Taxonomy Completion Method with Pre-trained Language Model

Fei Xia, Yixuan Weng, Shizhu He +2

Taxonomies, which organize domain concepts into hierarchical structures, are crucial for building knowledge systems and downstream applications. As domain knowledge evolves, taxono…

cs.CL2023★ 1 cited

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…

cs.CL2023★ 2 cited

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…

cs.CL2023

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…

cs.CL2023

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…