Search Results for author: Ashish Kulkarni

Found 6 papers, 2 papers with code

MATHSENSEI: A Tool-Augmented Large Language Model for Mathematical Reasoning

1 code implementation27 Feb 2024 Debrup Das, Debopriyo Banerjee, Somak Aditya, Ashish Kulkarni

While, TALMs have been successfully employed in different question-answering benchmarks, their efficacy on complex mathematical reasoning benchmarks, and the potential complementary benefits offered by tools for knowledge retrieval and mathematical equation solving are open research questions.

8k GPT-3.5 +6

MFBE: Leveraging Multi-Field Information of FAQs for Efficient Dense Retrieval

1 code implementation23 Feb 2023 Debopriyo Banerjee, Mausam Jain, Ashish Kulkarni

In the domain of question-answering in NLP, the retrieval of Frequently Asked Questions (FAQ) is an important sub-area which is well researched and has been worked upon for many languages.

Question Answering Retrieval

Extraction of Product Specifications from the Web -- Going Beyond Tables and Lists

no code implementations8 Jan 2022 Govind Krishnan Gangadhar, Ashish Kulkarni

In this paper, we present a product specification extraction approach that goes beyond tables or lists and generalizes across the diverse HTML elements used for rendering specification blocks.

Attribute Question Answering

Efficient Reuse of Structured and Unstructured Resources for Ontology Population

no code implementations LREC 2014 Chetana Gavankar, Ashish Kulkarni, Ganesh Ramakrishnan

A domain ontology for an organization, often consists of classes whose instances are either specific to, or independent of the organization.

Information Retrieval Retrieval +1

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