Cheng-Han Chiang (d223302)

d223302

User data from Github https://github.com/d223302

Company:SPML Lab at National Taiwan University

Location:Taipei, Taiwan

Home Page:https://d223302.github.io/

GitHub:@d223302

Cheng-Han Chiang's repositories

A-Closer-Look-To-LLM-Evaluation

Code for EMNLP 2023 findings paper "A Closer Look into Using Large Language Models for Automatic Evaluation"

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Over-Reasoning-of-LLMs

Data and code for EACL'24 paper: Over-Reasoning and Redundant Calculation of Large Language Models

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LLM-Evaluation

Can Large Language Models Be an Alternative to Human Evaluations?

Merging-Facts-Crafting-Fallacies

Code for paper "Merging Facts, Crafting Fallacies: Evaluating the Contradictory Nature of Aggregated Factual Claims in Long-Form Generations"

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ALCE

[EMNLP 2023] Enabling Large Language Models to Generate Text with Citations. Paper: https://arxiv.org/abs/2305.14627

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electra

ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators

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jiant

The jiant toolkit for general-purpose text understanding models

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Non-Targeted-Adversarial-Attacks

A non-targeted adversarial attack method, which won the first place in NIPS 2017 non-targeted adversarial attacks competition

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OptiPrompt

NAACL'2021: Factual Probing Is [MASK]: Learning vs. Learning to Recall

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SierraBreeze

OSX-like window decoration for KDE Plasma written in C++

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self-rag

This includes the original implementation of SELF-RAG: Learning to Retrieve, Generate and Critique through self-reflection by Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi.

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stanford-tensorflow-tutorials

This repository contains code examples for the Stanford's course: TensorFlow for Deep Learning Research.

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TextAttack

TextAttack 🐙 is a Python framework for adversarial attacks, data augmentation, and model training in NLP https://textattack.readthedocs.io/en/master/

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transformers

🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

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ZeroSpeech-TTS-without-T

A Pytorch implementation for the ZeroSpeech 2019 challenge.

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