29 citations · 33 across the 5 of their papers we have counts for
5 papers · 1 filter
Beyond Performance: Quantifying and Mitigating Label Bias in LLMs
Yuval Reif, Roy Schwartz
Large language models (LLMs) have shown remarkable adaptability to diverse tasks, by leveraging context prompts containing instructions, or minimal input-output examples. However,…
Localizing Open-Ontology QA Semantic Parsers in a Day Using Machine Translation
Mehrad Moradshahi, Giovanni Campagna, Sina J. Semnani +2
We propose Semantic Parser Localizer (SPL), a toolkit that leverages Neural Machine Translation (NMT) systems to localize a semantic parser for a new language. Our methodology is t…
Zero-Shot Transfer Learning with Synthesized Data for Multi-Domain Dialogue State Tracking
Giovanni Campagna, Agata Foryciarz, Mehrad Moradshahi +1
Zero-shot transfer learning for multi-domain dialogue state tracking can allow us to handle new domains without incurring the high cost of data acquisition. This paper proposes new…
HUBERT Untangles BERT to Improve Transfer across NLP Tasks
Mehrad Moradshahi, Hamid Palangi, Monica S. Lam +2
We introduce HUBERT which combines the structured-representational power of Tensor-Product Representations (TPRs) and BERT, a pre-trained bidirectional Transformer language model.…
Genie: A Generator of Natural Language Semantic Parsers for Virtual Assistant Commands
Giovanni Campagna, Silei Xu, Mehrad Moradshahi +2
To understand diverse natural language commands, virtual assistants today are trained with numerous labor-intensive, manually annotated sentences. This paper presents a methodology…