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pro vyhledávání: '"VARMA, VASUDEVA"'
Curriculum learning has been used to improve the quality of text generation systems by ordering the training samples according to a particular schedule in various tasks. In the context of data-to-text generation (DTG), previous studies used various d
Externí odkaz:
http://arxiv.org/abs/2412.13484
Traditional evaluation metrics like BLEU and ROUGE fall short when capturing the nuanced qualities of generated text, particularly when there is no single ground truth. In this paper, we explore the potential of Large Language Models (LLMs), specific
Externí odkaz:
http://arxiv.org/abs/2412.09269
This paper describes our approach for SemEval-2024 Task 9: BRAINTEASER: A Novel Task Defying Common Sense. The BRAINTEASER task comprises multiple-choice Question Answering designed to evaluate the models' lateral thinking capabilities. It consists o
Externí odkaz:
http://arxiv.org/abs/2405.16129
The proliferation of LLMs in various NLP tasks has sparked debates regarding their reliability, particularly in annotation tasks where biases and hallucinations may arise. In this shared task, we address the challenge of distinguishing annotations ma
Externí odkaz:
http://arxiv.org/abs/2405.11192
Hallucinations in large language models (LLMs) have recently become a significant problem. A recent effort in this direction is a shared task at Semeval 2024 Task 6, SHROOM, a Shared-task on Hallucinations and Related Observable Overgeneration Mistak
Externí odkaz:
http://arxiv.org/abs/2404.06948
Architectural Knowledge Management (AKM) involves the organized handling of information related to architectural decisions and design within a project or organization. An essential artifact of AKM is the Architecture Decision Records (ADR), which doc
Externí odkaz:
http://arxiv.org/abs/2403.01709
Autor:
Maity, Ankita, Sharma, Anubhav, Dhar, Rudra, Abhishek, Tushar, Gupta, Manish, Varma, Vasudeva
Lack of diverse perspectives causes neutrality bias in Wikipedia content leading to millions of worldwide readers getting exposed by potentially inaccurate information. Hence, neutrality bias detection and mitigation is a critical problem. Although p
Externí odkaz:
http://arxiv.org/abs/2312.15181
Autor:
Raha, Tathagata, Choudhary, Mukund, Menon, Abhinav, Gupta, Harshit, Srivatsa, KV Aditya, Gupta, Manish, Varma, Vasudeva
Factual consistency is one of the most important requirements when editing high quality documents. It is extremely important for automatic text generation systems like summarization, question answering, dialog modeling, and language modeling. Still,
Externí odkaz:
http://arxiv.org/abs/2306.08872
Autor:
Mehta, Rahul, Varma, Vasudeva
Named Entity Recognition(NER) is a task of recognizing entities at a token level in a sentence. This paper focuses on solving NER tasks in a multilingual setting for complex named entities. Our team, LLM-RM participated in the recently organized SemE
Externí odkaz:
http://arxiv.org/abs/2305.03300
Autor:
Taunk, Dhaval, Varma, Vasudeva
Publikováno v:
Forum for Information Retrieval Evaluation, December 9-13, 2022, India
With the advent of multilingual models like mBART, mT5, IndicBART etc., summarization in low resource Indian languages is getting a lot of attention now a days. But still the number of datasets is low in number. In this work, we (Team HakunaMatata) s
Externí odkaz:
http://arxiv.org/abs/2303.16657