Papers Table-based Question Answering
“Table-based Question Answering” 태그가 달린 논문 16편 · 필터 해제
MAPLE: Multi-Agent Adaptive Planning with Long-Term Memory for Table Reasoning
Table-based question answering requires complex reasoning capabilities that current LLMs struggle to achieve with single-pass inference. Existing approaches, such as Chain-of-Thought reasoning and question decomposition,…
Question AnsweringTable-based Question AnsweringWeaver: Interweaving SQL and LLM for Table Reasoning
Querying tables with unstructured data is challenging due to the presence of text (or image), either embedded in the table or in external paragraphs, which traditional SQL struggles to process, especially for tasks requi…
Question AnsweringTable-based Question AnsweringSynthetic Clarification and Correction Dialogues about Data-Centric Tasks -- A Teacher-Student Approach
Real dialogues with AI assistants for solving data-centric tasks often follow dynamic, unpredictable paths due to imperfect information provided by the user or in the data, which must be caught and handled. Developing da…
Question AnsweringTable-based Question AnsweringTabular-TX: Theme-Explanation Structure-based Table Summarization via In-Context Learning
This paper proposes a Theme-Explanation Structure-based Table Summarization (Tabular-TX) pipeline designed to efficiently process table data. Tabular-TX preprocesses table data by focusing on highlighted cells and then g…
In-Context LearningQuestion AnsweringTable-based Question AnsweringARTEMIS-DA: An Advanced Reasoning and Transformation Engine for Multi-Step Insight Synthesis in Data Analytics
This paper presents the Advanced Reasoning and Transformation Engine for Multi-Step Insight Synthesis in Data Analytics (ARTEMIS-DA), a novel framework designed to augment Large Language Models (LLMs) for solving complex…
Code GenerationInformation RetrievalQuestion AnsweringRetrieval+3Piece of Table: A Divide-and-Conquer Approach for Selecting Sub-Tables in Table Question Answering
Applying language models (LMs) to tables is challenging due to the inherent structural differences between two-dimensional tables and one-dimensional text for which the LMs were originally designed. Furthermore, when app…
Question AnsweringTable-based Question AnsweringSynTQA: Synergistic Table-based Question Answering via Mixture of Text-to-SQL and E2E TQA
Text-to-SQL parsing and end-to-end question answering (E2E TQA) are two main approaches for Table-based Question Answering task. Despite success on multiple benchmarks, they have yet to be compared and their synergy rema…
Answer SelectionQuestion AnsweringSemantic ParsingSQL Parsing+3Seek and Solve Reasoning for Table Question Answering
The complexities of table structures and question logic make table-based question answering (TQA) tasks challenging for Large Language Models (LLMs), often requiring task simplification before solving. This paper reveals…
In-Context LearningQuestion AnsweringTable-based Question AnsweringChain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding
Table-based reasoning with large language models (LLMs) is a promising direction to tackle many table understanding tasks, such as table-based question answering and fact verification. Compared with generic reasoning, ta…
Fact VerificationIn-Context LearningQuestion AnsweringSemantic Parsing+2TableQAKit: A Comprehensive and Practical Toolkit for Table-based Question Answering
Table-based question answering (TableQA) is an important task in natural language processing, which requires comprehending tables and employing various reasoning ways to answer the questions. This paper introduces TableQ…
Question AnsweringTable-based Question AnsweringBridging the Gap: Deciphering Tabular Data Using Large Language Model
In the realm of natural language processing, the understanding of tabular data has perpetually stood as a focal point of scholarly inquiry. The emergence of expansive language models, exemplified by the likes of ChatGPT,…
Language ModelingLanguage ModellingLarge Language ModelQuestion Answering+1OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering
The information in tables can be an important complement to text, making table-based question answering (QA) systems of great value. The intrinsic complexity of handling tables often adds an extra burden to both model de…
Question AnsweringRetrievalSemantic ParsingTable-based Question AnsweringTable Retrieval May Not Necessitate Table-specific Model Design
Tables are an important form of structured data for both human and machine readers alike, providing answers to questions that cannot, or cannot easily, be found in texts. Recent work has designed special models and train…
Hard AttentionNatural QuestionsQuestion AnsweringRetrieval+2Table Retrieval Does Not Necessitate Table-specific Model Design
Tables are an important form of structured data for both human and machine readers alike, providing answers to questions that cannot, or cannot easily, be found in texts. Recent work designs special models and trains for…
Natural QuestionsQuestion AnsweringRetrievalTable-based Question Answering+1Translating Natural Language to SQL using Pointer-Generator Networks and How Decoding Order Matters
Translating natural language to SQL queries for table-based question answering is a challenging problem and has received significant attention from the research community. In this work, we extend a pointer-generator and …
Question AnsweringSemantic ParsingTable-based Question AnsweringHow to Seq2seq for SQL
Translating natural language to SQL queries for table-based question answering has recently attracted more research attention. Previous approaches develop models whose architecture is specifically tuned to the structure …
Question AnsweringTable-based Question Answering