title: RoundTable: Leveraging Dynamic Schema and Contextual Autocomplete for
    Enhanced Query Precision in Tabular Question Answering
summary: With advancements in Large Language Models (LLMs), a major use case that has
  emerged is querying databases in plain English, translating user questions into
  executable database queries, which has improved significantly. However,
  real-world datasets often feature a vast array of attributes and complex
  values, complicating the LLMs task of accurately identifying relevant columns
  or values from natural language queries. Traditional methods cannot fully relay
  the datasets size and complexity to the LLM. To address these challenges, we
  propose a novel framework that leverages Full-Text Search (FTS) on the input
  table. This approach not only enables precise detection of specific values and
  columns but also narrows the search space for language models, thereby
  enhancing query accuracy. Additionally, it supports a custom auto-complete
  feature that suggests queries based on the data in the table. This integration
  significantly refines the interaction between the user and complex datasets,
  offering a sophisticated solution to the limitations faced by current table
  querying capabilities. This work is accompanied by an application for both Mac
  and Windows platforms, which readers can try out themselves on their own data.
paper_authors._id: 66cd14e4d688c0c8873c96ed
paper_authors.name: Pratyush Kumar
paper_authors._id: 66cd14e4d688c0c8873c96ee
paper_authors.name: Kuber Vijaykumar Bellad
paper_authors._id: 66cd14e4d688c0c8873c96ef
paper_authors.name: Bharat Vadlamudi
paper_authors._id: 66cd14e4d688c0c8873c96f0
paper_authors.name: Aman Chadha
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-08-27T07:42:24.914Z
paper_authors._id: 63a4754927f1f64ed7238dac
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63a4754927f1f64ed7238dac/aH-eJF-31g4vof9jv2gmI.jpeg
paper_authors.fullname: Aman Chadha
paper_authors.type: user
paper_authors.user: amanchadha
paper_title: RoundTable: Leveraging Dynamic Schema and Contextual Autocomplete for
    Enhanced Query Precision in Tabular Question Answering
paper_summary: With advancements in Large Language Models (LLMs), a major use case that has
  emerged is querying databases in plain English, translating user questions into
  executable database queries, which has improved significantly. However,
  real-world datasets often feature a vast array of attributes and complex
  values, complicating the LLMs task of accurately identifying relevant columns
  or values from natural language queries. Traditional methods cannot fully relay
  the datasets size and complexity to the LLM. To address these challenges, we
  propose a novel framework that leverages Full-Text Search (FTS) on the input
  table. This approach not only enables precise detection of specific values and
  columns but also narrows the search space for language models, thereby
  enhancing query accuracy. Additionally, it supports a custom auto-complete
  feature that suggests queries based on the data in the table. This integration
  significantly refines the interaction between the user and complex datasets,
  offering a sophisticated solution to the limitations faced by current table
  querying capabilities. This work is accompanied by an application for both Mac
  and Windows platforms, which readers can try out themselves on their own data.
paper_ai_keywords: Full-Text Search (FTS) LLMs Natural Language Queries Database Queries Custom Auto-Complete