3 citations · 5 across the 6 of their papers we have counts for
6 papers
Multimodal Self-Instruct: Synthetic Abstract Image and Visual Reasoning Instruction Using Language Model
Wenqi Zhang, Zhenglin Cheng, Yuanyu He +8
Although most current large multimodal models (LMMs) can already understand photos of natural scenes and portraits, their understanding of abstract images, e.g., charts, maps, or l…
Advancing Process Verification for Large Language Models via Tree-Based Preference Learning
Mingqian He, Yongliang Shen, Wenqi Zhang +2
Large Language Models (LLMs) have demonstrated remarkable potential in handling complex reasoning tasks by generating step-by-step rationales.Some methods have proven effective in…
An Expression Tree Decoding Strategy for Mathematical Equation Generation
Wenqi Zhang, Yongliang Shen, Qingpeng Nong +3
Generating mathematical equations from natural language requires an accurate understanding of the relations among math expressions. Existing approaches can be broadly categorized i…
MProto: Multi-Prototype Network with Denoised Optimal Transport for Distantly Supervised Named Entity Recognition
Shuhui Wu, Yongliang Shen, Zeqi Tan +4
Distantly supervised named entity recognition (DS-NER) aims to locate entity mentions and classify their types with only knowledge bases or gazetteers and unlabeled corpus. However…
PromptNER: Prompt Locating and Typing for Named Entity Recognition
Yongliang Shen, Zeqi Tan, Shuhui Wu +5
Prompt learning is a new paradigm for utilizing pre-trained language models and has achieved great success in many tasks. To adopt prompt learning in the NER task, two kinds of met…
DAMO-NLP at SemEval-2023 Task 2: A Unified Retrieval-augmented System for Multilingual Named Entity Recognition
Zeqi Tan, Shen Huang, Zixia Jia +8
The MultiCoNER \RNum{2} shared task aims to tackle multilingual named entity recognition (NER) in fine-grained and noisy scenarios, and it inherits the semantic ambiguity and low-c…