RetrieveGPT: Merging Prompts and Mathematical Models for Enhanced
  Code-Mixed Information Retrieval
Authors: name: Aniket Deroy, name: Subhankar Maity

Code-mixing, the integration of lexical and grammatical elements from
multiple languages within a single sentence, is a widespread linguistic
phenomenon, particularly prevalent in multilingual societies. In India, social
media users frequently engage in code-mixed conversations using the Roman
script, especially among migrant communities who form online groups to share
relevant local information. This paper focuses on the challenges of extracting
relevant information from code-mixed conversations, specifically within Roman
transliterated Bengali mixed with English. This study presents a novel approach
to address these challenges by developing a mechanism to automatically identify
the most relevant answers from code-mixed conversations. We have experimented
with a dataset comprising of queries and documents from Facebook, and Query
Relevance files (QRels) to aid in this task. Our results demonstrate the
effectiveness of our approach in extracting pertinent information from complex,
code-mixed digital conversations, contributing to the broader field of natural
language processing in multilingual and informal text environments. We use
GPT-3.5 Turbo via prompting alongwith using the sequential nature of relevant
documents to frame a mathematical model which helps to detect relevant
documents corresponding to a query.