Sheet as Token represents each worksheet as a single dense token and uses a multi-channel graph retriever to improve retrieval of supporting sheets in multi-sheet workbooks.
Retrieval-augmented generation for knowledge-intensive NLP tasks
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3representative citing papers
Combining local routing with prompt compression saves 45-79% cloud tokens on edit and explanation workloads, while a fuller set including draft-review saves 51% on RAG-heavy tasks.
CUE-R uses REMOVE, REPLACE, and DUPLICATE interventions on individual evidence items to quantify their per-item utility in RAG along correctness, grounding faithfulness, and confidence axes.
citing papers explorer
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Sheet as Token: A Graph-Enhanced Representation for Multi-Sheet Spreadsheet Understanding
Sheet as Token represents each worksheet as a single dense token and uses a multi-channel graph retriever to improve retrieval of supporting sheets in multi-sheet workbooks.
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Local-Splitter: A Measurement Study of Seven Tactics for Reducing Cloud LLM Token Usage on Coding-Agent Workloads
Combining local routing with prompt compression saves 45-79% cloud tokens on edit and explanation workloads, while a fuller set including draft-review saves 51% on RAG-heavy tasks.
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CUE-R: Beyond the Final Answer in Retrieval-Augmented Generation
CUE-R uses REMOVE, REPLACE, and DUPLICATE interventions on individual evidence items to quantify their per-item utility in RAG along correctness, grounding faithfulness, and confidence axes.