https://github.com/AI4LIFE-GROUP/LLM_Explainer
공식 코드In-Context Explainers: Harnessing LLMs for Explaining Black Box Models
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
XAI refers to methods and techniques in the application of artificial intelligence (AI) such that the results of the solution can be understood by humans. It contrasts with the concept of the "black box" in machine learning where even its designers cannot explain why an AI arrived at a specific decision. XAI may be an implementation of the social right to explanation. XAI is relevant even if there is no legal right or regulatory requirement—for example, XAI can improve the user experience of a product or service by helping end users trust that the AI is making good decisions. This way the aim of XAI is to explain what has been done, what is done right now, what will be done next and unveil the information the actions are based on. These characteristics make it possible (i) to confirm existing knowledge (ii) to challenge existing knowledge and (iii) to generate new assumptions.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
In-Context Explainers: Harnessing LLMs for Explaining Black Box Models
Explainability in Process Outcome Prediction: Guidelines to Obtain Interpretable and Faithful Models
Towards Musically Meaningful Explanations Using Source Separation
Mitigating belief projection in explainable artificial intelligence via Bayesian Teaching
CoXQL: A Dataset for Parsing Explanation Requests in Conversational XAI Systems
Towards Rigorous Interpretations: a Formalisation of Feature Attribution
A Comprehensive Guide to Explainable AI: From Classical Models to LLMs
Landscape of R packages for eXplainable Artificial Intelligence
Developing a Fidelity Evaluation Approach for Interpretable Machine Learning
Towards Explainable Multimodal Depression Recognition for Clinical Interviews
SoPa: Bridging CNNs, RNNs, and Weighted Finite-State Machines
Interpretable Summaries of Black Box Incident Triaging with Subgroup Discovery
GNNExplainer: Generating Explanations for Graph Neural Networks
Multimodal Explanations: Justifying Decisions and Pointing to the Evidence
Leveraging CAM Algorithms for Explaining Medical Semantic Segmentation
TSEM: Temporally Weighted Spatiotemporal Explainable Neural Network for Multivariate Time Series
The Effect of Balancing Methods on Model Behavior in Imbalanced Classification Problems
The Effect of Balancing Methods on Model Behavior in Imbalanced Classification Problems
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.