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Music Question Answering

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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하위 태스크

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평가표

1

보존된 평가 기록

3

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/YuanGongND/ltu

공식 코드

Listen, Think, and Understand

https://github.com/crypto-code/mu-llama

공식 코드

Music Understanding LLaMA: Advancing Text-to-Music Generation with Question Answering and Captioning

https://github.com/mulab-mir/muchomusic

공식 코드

MuChoMusic: Evaluating Music Understanding in Multimodal Audio-Language Models

https://github.com/opengvlab/llama-adapter

공식 코드

LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention

https://github.com/zrrskywalker/llama-adapter

공식 코드

LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention

https://github.com/Lightning-AI/lit-llama

LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention

https://github.com/alpha-vllm/llama2-accessory

LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention

https://github.com/audiollms/audiobench

MuChoMusic: Evaluating Music Understanding in Multimodal Audio-Language Models

https://github.com/shansongliu/M2UGen

Music Understanding LLaMA: Advancing Text-to-Music Generation with Question Answering and Captioning

https://github.com/shansongliu/MU-LLaMA

Music Understanding LLaMA: Advancing Text-to-Music Generation with Question Answering and Captioning

https://github.com/zihanzhaosjtu/librisqa

LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention

https://github.com/ziyuguo99/point-bind_point-llm

LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention

https://github.com/zrrskywalker/point-bind

LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.