no code implementations • 26 May 2024 • Piyush Jha, Prithwish Jana, Arnav Arora, Vijay Ganesh
In recent years, large language models (LLMs) have had a dramatic impact on various sub-fields of AI, most notably on natural language understanding tasks.
no code implementations • 8 Jan 2024 • Aatman Vaidya, Arnav Arora, Aditya Joshi, Tarunima Prabhakar
For the test set, approximately 1200 posts were provided.
2 code implementations • 15 Nov 2023 • Yuxia Wang, Revanth Gangi Reddy, Zain Muhammad Mujahid, Arnav Arora, Aleksandr Rubashevskii, Jiahui Geng, Osama Mohammed Afzal, Liangming Pan, Nadav Borenstein, Aditya Pillai, Isabelle Augenstein, Iryna Gurevych, Preslav Nakov
The increased use of large language models (LLMs) across a variety of real-world applications calls for mechanisms to verify the factual accuracy of their outputs.
no code implementations • 15 Nov 2023 • Arnav Arora, Maha Jinadoss, Cheshta Arora, Denny George, Brindaalakshmi, Haseena Dawood Khan, Kirti Rawat, Div, Ritash, Seema Mathur, Shivani Yadav, Shehla Rashid Shora, Rie Raut, Sumit Pawar, Apurva Paithane, Sonia, Vivek, Dharini Priscilla, Khairunnisha, Grace Banu, Ambika Tandon, Rishav Thakker, Rahul Dev Korra, Aatman Vaidya, Tarunima Prabhakar
In this paper we present a dataset on gendered abuse in three languages- Hindi, Tamil and Indian English.
1 code implementation • 9 Oct 2023 • Lucie-Aimée Kaffee, Arnav Arora, Isabelle Augenstein
The moderation of content on online platforms is usually non-transparent.
2 code implementations • 1 Jun 2023 • Erik Arakelyan, Arnav Arora, Isabelle Augenstein
The results show that our method outperforms the state-of-the-art with an average of $3. 5$ F1 points increase in-domain, and is more generalizable with an averaged increase of $10. 2$ F1 on out-of-domain evaluation while using $\leq10\%$ of the training data.
Ranked #1 on Stance Detection on mtsd
1 code implementation • 17 Apr 2023 • Lucie-Aimée Kaffee, Arnav Arora, Zeerak Talat, Isabelle Augenstein
Dual use, the intentional, harmful reuse of technology and scientific artefacts, is a problem yet to be well-defined within the context of Natural Language Processing (NLP).
1 code implementation • 25 Mar 2022 • Arnav Arora, Lucie-Aimée Kaffee, Isabelle Augenstein
In this paper, we introduce probes to study which values across cultures are embedded in these models, and whether they align with existing theories and cross-cultural value surveys.
1 code implementation • 13 Sep 2021 • Momchil Hardalov, Arnav Arora, Preslav Nakov, Isabelle Augenstein
Most research in stance detection, however, has been limited to working with a single language and on a few limited targets, with little work on cross-lingual stance detection.
2 code implementations • EMNLP 2021 • Momchil Hardalov, Arnav Arora, Preslav Nakov, Isabelle Augenstein
In this paper, we perform an in-depth analysis of 16 stance detection datasets, and we explore the possibility for cross-domain learning from them.
no code implementations • Findings (NAACL) 2022 • Momchil Hardalov, Arnav Arora, Preslav Nakov, Isabelle Augenstein
Understanding attitudes expressed in texts, also known as stance detection, plays an important role in systems for detecting false information online, be it misinformation (unintentionally false) or disinformation (intentionally false information).
no code implementations • 27 Feb 2021 • Arnav Arora, Preslav Nakov, Momchil Hardalov, Sheikh Muhammad Sarwar, Vibha Nayak, Yoan Dinkov, Dimitrina Zlatkova, Kyle Dent, Ameya Bhatawdekar, Guillaume Bouchard, Isabelle Augenstein
The proliferation of harmful content on online platforms is a major societal problem, which comes in many different forms including hate speech, offensive language, bullying and harassment, misinformation, spam, violence, graphic content, sexual abuse, self harm, and many other.
1 code implementation • 10 Sep 2020 • Wojciech Ostrowski, Arnav Arora, Pepa Atanasova, Isabelle Augenstein
We: 1) construct a small annotated dataset, PolitiHop, of evidence sentences for claim verification; 2) compare it to existing multi-hop datasets; and 3) study how to transfer knowledge from more extensive in- and out-of-domain resources to PolitiHop.