REVIEW 2 cited by
CAESURA: Language Models as Multi-Modal Query Planners
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Traditional query planners translate SQL queries into query plans to be executed over relational data. However, it is impossible to query other data modalities, such as images, text, or video stored in modern data systems such as data lakes using these query planners. In this paper, we propose Language-Model-Driven Query Planning, a new paradigm of query planning that uses Language Models to translate natural language queries into executable query plans. Different from relational query planners, the resulting query plans can contain complex operators that are able to process arbitrary modalities. As part of this paper, we present a first GPT-4 based prototype called CEASURA and show the general feasibility of this idea on two datasets. Finally, we discuss several ideas to improve the query planning capabilities of today's Language Models.
Forward citations
Cited by 2 Pith papers
-
Deep Research is the New Analytics System: Towards Building the Runtime for AI-Driven Analytics
A prototype combining Deep Research planning with Palimpzest-style semantic operator optimization beats open-code agents on two unstructured analytics queries.
-
SEFRQO: A Self-Evolving Fine-Tuned RAG-Based Query Optimizer
SEFRQO claims a self-evolving fine-tuned LLM with retrieval and execution feedback reduces query latency versus PostgreSQL, but the provided body is a different paper, blocking verification.
Discussion (0). Sign in to comment.