Efficiency of Large Language Models to scale up Ground Truth: Overview of the IRSE Track at Forum for Information Retrieval 2023-Reference-Cited by-同舟云学术

Efficiency of Large Language Models to scale up Ground Truth: Overview of the IRSE Track at Forum for Information Retrieval 2023

Published:2023-12-15 Issue: Volume:14 Page:16-18
ISSN:
Container-title:Proceedings of the 15th Annual Meeting of the Forum for Information Retrieval Evaluation
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Author:

Paul Soumen¹^ORCID,Majumdar Srijoni²^ORCID,Bandyopadhyay Ayan³^ORCID,Dave Bhargav⁴^ORCID,Chattopadhyay Samiran⁵^ORCID,Das Partha⁶^ORCID,Clough Paul D⁷^ORCID,Majumder Prasenjit⁸^ORCID

Affiliation:

1. Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, India

2. Indian Institute of Technology Kharagpur, India and University of Leeds, UK

3. TCG CREST, India

4. DA-IICT, India

5. Jadavpur University, India

6. Indian Institute of Technology Kharagpur, India

7. University of Sheffield, UK and TPXimpact London, UK

8. TCG CREST, India and DA-IICT, India

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3632754.3633480

Reference14 articles.

1. Amiangshu Bosu, Michaela Greiler, and Christian Bird. 2015. Characteristics of useful code reviews: An empirical study at microsoft(Working Conference on Mining Software Repositories). IEEE, 146–156.

2. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, 2020. Language models are few-shot learners. Advances in neural information processing systems 33 (2020), 1877–1901.

3. Nachiketa Chatterjee, Srijoni Majumdar, Shila Rani Sahoo, and Partha Pratim Das. 2015. Debugging multi-threaded applications using pin-augmented gdb (pgdb). In International conference on software engineering research and practice (SERP). Springer. 109–115.

4. Srijoni Majumdar Ayan Bandyopadhyay Samiran Chattopadhyay Partha Pratim Das Paul D Clough and Prasenjit Majumder. 2022. Overview of the IRSE track at FIRE 2022: Information Retrieval in Software Engineering. In Forum for Information Retrieval Evaluation ACM.

5. Can we predict useful comments in source codes? - Analysis of findings from Information Retrieval in Software Engineering Track @ FIRE 2022