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Ananya Singha

3 papers here

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author1
  • middle author2

Across the 3 of 3 papers where every author was matched, so the position is known.

fields
  • cs.AI1
  • cs.CL1
  • cs.HC1
ORCID 0009-0009-7682-611X

identity via Semantic Scholar / OpenAlex

most citedConversational Challenges in AI-Powered Data Science: Obstacles, Needs, and Design Opportunities

2 citations · 4 across the 3 of their papers we have counts for

collaborators

3 papers

cs.AI2023

TSTR: Target Similarity Tuning Meets the Real World

Anirudh Khatry, Sumit Gulwani, Priyanshu Gupta +4

Target similarity tuning (TST) is a method of selecting relevant examples in natural language (NL) to code generation through large language models (LLMs) to improve performance. I…

cs.HC2023★ 2 cited

Conversational Challenges in AI-Powered Data Science: Obstacles, Needs, and Design Opportunities

Bhavya Chopra, Ananya Singha, Anna Fariha +4

Large Language Models (LLMs) are being increasingly employed in data science for tasks like data preprocessing and analytics. However, data scientists encounter substantial obstacl…

cs.CL2023★ 2 cited

Tabular Representation, Noisy Operators, and Impacts on Table Structure Understanding Tasks in LLMs

Ananya Singha, José Cambronero, Sumit Gulwani +2

Large language models (LLMs) are increasingly applied for tabular tasks using in-context learning. The prompt representation for a table may play a role in the LLMs ability to proc…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.