BrainStorm @ iREL at #SMM4H 2024: Leveraging Translation and Topical Embeddings for Annotation Detection in Tweets

Fuente: arXiv
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Autori principali: Chaudhary, Manav, Gupta, Harshit, Varma, Vasudeva
Natura: Preprint
Pubblicazione: 2024
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author Chaudhary, Manav
Gupta, Harshit
Varma, Vasudeva
author_facet Chaudhary, Manav
Gupta, Harshit
Varma, Vasudeva
contents 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 made by LLMs from those made by human domain experts in the context of COVID-19 symptom detection from tweets in Latin American Spanish. This paper presents BrainStorm @ iRELs approach to the SMM4H 2024 Shared Task, leveraging the inherent topical information in tweets, we propose a novel approach to identify and classify annotations, aiming to enhance the trustworthiness of annotated data.
format Preprint
id arxiv_https___arxiv_org_abs_2405_11192
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle BrainStorm @ iREL at #SMM4H 2024: Leveraging Translation and Topical Embeddings for Annotation Detection in Tweets
Chaudhary, Manav
Gupta, Harshit
Varma, Vasudeva
Computation and Language
Social and Information Networks
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 made by LLMs from those made by human domain experts in the context of COVID-19 symptom detection from tweets in Latin American Spanish. This paper presents BrainStorm @ iRELs approach to the SMM4H 2024 Shared Task, leveraging the inherent topical information in tweets, we propose a novel approach to identify and classify annotations, aiming to enhance the trustworthiness of annotated data.
title BrainStorm @ iREL at #SMM4H 2024: Leveraging Translation and Topical Embeddings for Annotation Detection in Tweets
topic Computation and Language
Social and Information Networks
url https://arxiv.org/abs/2405.11192