AutoPrep: Natural Language Question-Aware Data Preparation with a Multi-Agent Framework
Answering natural language (NL) questions about tables, known as Tabular Question Answering (TQA), is crucial because it allows users to quickly and efficiently extract meaningful insights from structured data, effectively bridging the gap between human language and machine-readable formats. Many of these tables are derived from web sources or real-world scenarios, which require meticulous data preparation (or data prep) to ensure accurate responses. However, preparing such tables for NL questions introduces new requirements that extend beyond traditional data preparation. This question-aware data preparation involves specific tasks such as column derivation and filtering tailored to particular questions, as well as question-aware value normalization or conversion, highlighting the need for a more nuanced approach in this context. Because each of the above tasks is unique, a single model (or agent) may not perform effectively across all scenarios. In this paper, we propose AutoPrep, a large language model (LLM)-based multi-agent framework that leverages the strengths of multiple agents, each specialized in a certain type of data prep, ensuring more accurate and contextually relevant responses. Given an NL question over a table, AutoPrep performs data prep through three key components. Planner: Determines a logical plan, outlining a sequence of high-level operations. Programmer: Translates this logical plan into a physical plan by generating the corresponding low-level code. Executor: Executes the generated code to process the table. To support this multi-agent framework, we design a novel Chain-of-Clauses reasoning mechanism for high-level operation suggestion, and a tool-augmented method for low-level code generation...
Code (0)
등록된 구현이 없습니다.
Tasks
Code GenerationLarge Language ModelQuestion AnsweringSimilar Papers 제목 키워드 기반
AutoPrep: An Automatic Preprocessing Framework for In-the-Wild Speech Data
Recently, the utilization of extensive open-sourced text data has significantly advanced the performance of text-based large language models (LLMs). However, the use of in-the-wild large-scale speech data in the speech t…
Automatic Speech RecognitionSpeech EnhancementSpeech Extractionspeech-recognition+1ConceptBert: Concept-Aware Representation for Visual Question Answering
Visual Question Answering (VQA) is a challenging task that has received increasing attention from both the computer vision and the natural language processing communities. A VQA model combines visual and textual features…
Common Sense ReasoningQuestion AnsweringVisual Question AnsweringVisual Question Answering (VQA)Generating Context-Aware Natural Answers for Questions in 3D Scenes
3D question answering is a young field in 3D vision-language that is yet to be explored. Previous methods are limited to a pre-defined answer space and cannot generate answers naturally. In this work, we pivot the questi…
Question AnsweringSentenceSADGA: Structure-Aware Dual Graph Aggregation Network for Text-to-SQL
The Text-to-SQL task, aiming to translate the natural language of the questions into SQL queries, has drawn much attention recently. One of the most challenging problems of Text-to-SQL is how to generalize the trained mo…
Semantic ParsingText to SQLText-To-SQLText-Aware Dual Routing Network for Visual Question Answering
Visual question answering (VQA) is a challenging task to provide an accurate natural language answer given an image and a natural language question about the image. It involves multi-modal learning, i.e., computer vision…
Optical Character RecognitionOptical Character Recognition (OCR)Question AnsweringVisual Question Answering+1