RoundTable: Leveraging Dynamic Schema and Contextual Autocomplete for
  Enhanced Query Precision in Tabular Question Answering
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.
66cd14e4d688c0c8873c96ed
Pratyush Kumar
66cd14e4d688c0c8873c96ee
Kuber Vijaykumar Bellad
66cd14e4d688c0c8873c96ef
Bharat Vadlamudi
66cd14e4d688c0c8873c96f0
Aman Chadha
claimed_verified
2024-08-27T07:42:24.914Z
63a4754927f1f64ed7238dac
https://cdn-avatars.huggingface.co/v1/production/uploads/63a4754927f1f64ed7238dac/aH-eJF-31g4vof9jv2gmI.jpeg
Aman Chadha
user
amanchadha
RoundTable: Leveraging Dynamic Schema and Contextual Autocomplete for
  Enhanced Query Precision in Tabular Question Answering
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.
A framework integrating Full-Text Search into Large Language Models enhances the accuracy of translating natural language queries into database queries, especially for complex datasets.
Large Language Models Full-Text Search natural language queries database queries complex datasets