BrainStorm @ iREL at #SMM4H 2024: Leveraging Translation and Topical Embeddings for Annotation Detection in Tweets
<|reference_start|>BrainStorm @ iREL at #SMM4H 2024: Leveraging Translation and Topical Embeddings for Annotation Detection in Tweets: 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.<|reference_end|>
@article{chaudhary2024brainstorm,
    title={BrainStorm @ iREL at #SMM4H 2024: Leveraging Translation and Topical
  Embeddings for Annotation Detection in Tweets},
    author={Manav Chaudhary and Harshit Gupta and Vasudeva Varma},
    journal={arXiv preprint arXiv:2405.11192},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.11192},
    primaryClass={cs.CL cs.SI}
}