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Hi 👋, I'm Zhehao Zhang

A first-year Master student in Computer Science at Dartmouth College, focusing on NLP.

Coding

zzh-sjtu

  • 🔭 I’m currently working on LLMs' social aspects and applications on structured data

  • 🌱 I’m currently learning Langchain

  • 👨‍💻 All of my projects are available at https://github.com/zzh-SJTU?tab=repositories

  • 💬 Ask me about Large language models (LLMs) and socially-aware NLP

  • 📫 How to reach me [email protected]

Connect with me:

Languages and Tools:

arduino c cplusplus csharp git hexo jekyll linux matlab opencv pandas python pytorch scikit_learn seaborn tensorflow

zzh-sjtu

Zhehao Zhang's Projects

crt-qa icon crt-qa

The official data and code for EMNLP 2023 main conference paper: CRT-QA: A Dataset of Complex Reasoning Question Answering over Tabular Data

e5-hierarchical-table-analysis icon e5-hierarchical-table-analysis

The official code for NAACL 2024 paper: $E^5$: Zero-shot Hierarchical Table Analysis using Augmented LLMs via Explain, Extract, Execute, Exhibit and Extrapolate

examples icon examples

A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.

hough_transform icon hough_transform

Implement hough transform to find circles in images. (sequence labeling, edge detector)

language_modeling icon language_modeling

Using multiple models (LSTM, GRU, Transformer) to perform language modeling using limited data.

llm-reasoning-papers icon llm-reasoning-papers

Collection of papers and resources on Reasoning in Large Language Models (LLMs), including Chain-of-Thought (CoT), Instruction-Tuning, and others.

mnist_tutorial icon mnist_tutorial

A tutorial for MNIST handwritten digit classification using sklearn, PyTorch and Keras.

ner_chinese_medical icon ner_chinese_medical

Chinese medical named entity recognition using roberta + Flat model + adversarial training.

sift_implement icon sift_implement

A simplified version of sift including blob detection, computing descriptor, RANSAC algorithm and image alignment.

slu-with-pretrained-model icon slu-with-pretrained-model

Spoken Language Understanding. 口语语义理解是对话系统中重要的一环,其目的在于从用户的话语中提取出用户的意图,以为后续模块提供信息,最终顺利完成用户的任务。一般情况下,我们使用若干语义单元来描述用户的语义,每个语义单元由动作(act),语义槽(slot),槽值(value)三个部分构成。

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