24 citations · 38 across the 12 of their papers we have counts for
13 papers
Pluralis v0.1: Towards a Multicultural, Multimodal, Multilingual Benchmark for AI Risk and Reliability
Alicia Parrish, Rajat Shinde, Sanket Badhe +57
Current AI safety evaluation and benchmarking frameworks predominantly rely on Western-centric culture-agnostic defaults that mask critical regional laws, socio-linguistic nuances,…
Dial HEALTHDIAL for Advice: A Multilingual and Multi-Parallel Spoken Dialogue Dataset for Knowledge-Grounded Information Seeking
Songbo Hu, Yinhong Liu, Ej Zhou +5
Creating spoken dialogue datasets is methodologically challenging, and these challenges are amplified when the goal is to build multilingual, multi-parallel datasets at scale. This…
Going PLACES: Participatory Localized Red Teaming for Text-to-Image Safety in the Global South
Charvi Rastogi, Mukul Bhutani, Minsuk Kahng +13
Despite the global deployment of text-to-image (T2I) models, their safety frameworks are largely calibrated to a Western-centric default, creating significant vulnerabilities for t…
Analyzing and Adapting Large Language Models for Few-Shot Multilingual NLU: Are We There Yet?
Evgeniia Razumovskaia, Ivan Vulić, Anna Korhonen
Supervised fine-tuning (SFT), supervised instruction tuning (SIT) and in-context learning (ICL) are three alternative, de facto standard approaches to few-shot learning. ICL has ga…
: Improving Factuality of Information-Seeking Dialogue via Behavioural Fine-Tuning
Evgeniia Razumovskaia, Ivan Vulić, Pavle Marković +4
Factuality is a crucial requirement in information seeking dialogue: the system should respond to the user's queries so that the responses are meaningful and aligned with the knowl…
SQATIN: Supervised Instruction Tuning Meets Question Answering for Improved Dialogue NLU
Evgeniia Razumovskaia, Goran Glavaš, Anna Korhonen +1
Task-oriented dialogue (ToD) systems help users execute well-defined tasks across a variety of domains (e.g., or ), with their Nat…
Transfer-Free Data-Efficient Multilingual Slot Labeling
Evgeniia Razumovskaia, Ivan Vulić, Anna Korhonen
Slot labeling (SL) is a core component of task-oriented dialogue (ToD) systems, where slots and corresponding values are usually language-, task- and domain-specific. Therefore, ex…
Little Red Riding Hood Goes Around the Globe:Crosslingual Story Planning and Generation with Large Language Models
Evgeniia Razumovskaia, Joshua Maynez, Annie Louis +2
Previous work has demonstrated the effectiveness of planning for story generation exclusively in a monolingual setting focusing primarily on English. We consider whether planning b…
MULTI3NLU++: A Multilingual, Multi-Intent, Multi-Domain Dataset for Natural Language Understanding in Task-Oriented Dialogue
Nikita Moghe, Evgeniia Razumovskaia, Liane Guillou +3
Task-oriented dialogue (TOD) systems have been widely deployed in many industries as they deliver more efficient customer support. These systems are typically constructed for a sin…
Cross-Lingual Dialogue Dataset Creation via Outline-Based Generation
Olga Majewska, Evgeniia Razumovskaia, Edoardo Maria Ponti +2
Multilingual task-oriented dialogue (ToD) facilitates access to services and information for many (communities of) speakers. Nevertheless, the potential of this technology is not f…
Crossing the Conversational Chasm: A Primer on Natural Language Processing for Multilingual Task-Oriented Dialogue Systems
Evgeniia Razumovskaia, Goran Glavaš, Olga Majewska +3
In task-oriented dialogue (ToD), a user holds a conversation with an artificial agent to complete a concrete task. Although this technology represents one of the central objectives…
Pretraining Methods for Dialog Context Representation Learning
Shikib Mehri, Evgeniia Razumovskaia, Tiancheng Zhao +1
This paper examines various unsupervised pretraining objectives for learning dialog context representations. Two novel methods of pretraining dialog context encoders are proposed,…
Beyond Turing: Intelligent Agents Centered on the User
Maxine Eskenazi, Shikib Mehri, Evgeniia Razumovskaia +1
Most research on intelligent agents centers on the agent and not on the user. We look at the origins of agent-centric research for slot-filling, gaming and chatbot agents. We then…