title: RetrieveGPT: Merging Prompts and Mathematical Models for Enhanced
    Code-Mixed Information Retrieval
summary: 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.
paper_authors._id: 672d9199aa3c1b10a55aad9d
paper_authors.name: Aniket Deroy
paper_authors._id: 672d9199aa3c1b10a55aad9e
paper_authors.name: Subhankar Maity
paper_title: RetrieveGPT: Merging Prompts and Mathematical Models for Enhanced
    Code-Mixed Information Retrieval
paper_summary: 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.
paper_ai_summary: This paper develops an approach using GPT-3.5 Turbo to extract relevant information from code-mixed conversations, particularly Roman transliterated Bengali mixed with English on social media.
paper_ai_keywords: GPT-3.5 Turbo code-mixed conversations Roman transliterated Bengali English natural language processing multilingual informal text Query Relevance files QRels mathematical model