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cs.CL2024
RAGViz: Diagnose and Visualize Retrieval-Augmented Generation
Tevin Wang, Jingyuan He, Chenyan Xiong
Retrieval-augmented generation (RAG) combines knowledge from domain-specific sources into large language models to ground answer generation. Current RAG systems lack customizable v…
cs.IR2024
Interpret and Control Dense Retrieval with Sparse Latent Features
Hao Kang, Tevin Wang, Chenyan Xiong
Dense embeddings deliver strong retrieval performance but often lack interpretability and controllability. This paper introduces a novel approach using sparse autoencoders (SAE) to…
cs.AI2024
ResearchArena: Benchmarking Large Language Models' Ability to Collect and Organize Information as Research Agents
Hao Kang, Chenyan Xiong
Large language models (LLMs) excel across many natural language processing tasks but face challenges in domain-specific, analytical tasks such as conducting research surveys. This…