2 citations · 2 across the 9 of their papers we have counts for
11 papers
Decomposing and Predicting Annotation Disagreement in Harmful Content Across Languages
Irem Ecem Yelkanat, Faeze Ghorbanpour, Alexander Fraser
Annotator disagreement is increasingly recognized as a meaningful property of subjective tasks, rather than merely noise to be removed. Predicting which items are likely to be cont…
Retrieval Disagreement as a Signal: Diagnosing and Steering Multilingual Dense Embedders
Faeze Ghorbanpour, Shaghayegh Kolli, Jana Diesner +1
Multilingual dense retrievers place all languages in one shared space so that a question finds relevant documents, regardless of which language they are written in. We show that th…
Latent-Space Intervention for Cross-Lingual Factual Consistency: Consistency Improvements without Accuracy Drops
Faeze Ghorbanpour, Constanza Fierro, Alexander Fraser +1
Large Language Models (LLMs) often answer the same factual question differently across languages. We study whether cross-lingual latent-space intervention can reduce this inconsist…
Attribute-Based Diagnosis of LLM Alignment with Hate Speech Annotations
Mohammad Amine Jradi, Faeze Ghorbanpour, Alexander Fraser
Hate speech annotation is costly, subjective, and prone to annotator disagreement, making large-scale dataset construction challenging. We systematically analyze how well large lan…
PersLitEval: Fine-grained Benchmark and Evaluation of LLMs on Persian Literature Questions
Ruhallah Niazi, Faeze Ghorbanpour, Alexander Fraser
Despite impressive multilingual capabilities, large language models (LLMs) remain poorly evaluated on literary knowledge in non-English languages. We introduce PersLitEval, a bench…
On the Sensitivity of Instruction-tuned LLMs to Harmful Sentences in Long Inputs
Faeze Ghorbanpour, Alexander Fraser
Large language models (LLMs) increasingly operate on long inputs, yet their behavior when harmful sentences are sparsely embedded within such inputs remains poorly understood. We p…
Data-Efficient Hate Speech Detection via Cross-Lingual Nearest Neighbor Retrieval with Limited Labeled Data
Faeze Ghorbanpour, Daryna Dementieva, Alexander Fraser
Considering the importance of detecting hateful language, labeled hate speech data is expensive and time-consuming to collect, particularly for low-resource languages. Prior work h…
Can Prompting LLMs Unlock Hate Speech Detection across Languages? A Zero-shot and Few-shot Study
Faeze Ghorbanpour, Daryna Dementieva, Alexander Fraser
Despite growing interest in automated hate speech detection, most existing approaches overlook the linguistic diversity of online content. Multilingual instruction-tuned large lang…
Differentiating Emigration from Return Migration of Scholars Using Name-Based Nationality Detection Models
Faeze Ghorbanpour, Thiago Zordan Malaguth, Aliakbar Akbaritabar
Most web and digital trace data do not include information about an individual's nationality due to privacy concerns. The lack of data on nationality can create challenges for migr…
Are BabyLMs Second Language Learners?
Lukas Edman, Lisa Bylinina, Faeze Ghorbanpour +1
This paper describes a linguistically-motivated approach to the 2024 edition of the BabyLM Challenge (Warstadt et al. 2023). Rather than pursuing a first language learning (L1) par…
FNR: A Similarity and Transformer-Based Approach to Detect Multi-Modal Fake News in Social Media
Faeze Ghorbanpour, Maryam Ramezani, Mohammad A. Fazli +1
The availability and interactive nature of social media have made them the primary source of news around the globe. The popularity of social media tempts criminals to pursue their…