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Shubhi Tyagi

As seen in: Amazon Science
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Articles

HALLUCANA: Fixing LLM hallucination with a canary lookahead

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By Tianyi Li, Shubhi Tyagi, Andrea Pierleoni, Erenay Dayanik
| Amazon Science
In this paper, we present HALLUCANA, a canary lookahead to detect and correct factuality hallucinations of Large Language Models (LLMs) in long-form generation. HALLUCANA detects and intervenes as soon as traces of hallucination emerge, during and even before generation.
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Proteno: Text normalization with limited data for fast deployment in text-to-speech systems

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By Shubhi Tyagi, Antonio Bonafonte, Jaime Lorenzo Trueba, Javier Giralt Latorre
| Amazon Science
Shubhi Tyagi, Antonio Bonafonte, Jaime Lorenzo Trueba, Javier Latorre Copy BibTeX Developing Text Normalization (TN) systems for Text-to-Speech (TTS) on new languages is hard. We propose a novel architecture to facilitate it for multiple languages while using data less than 3% of the size of the data used by the state of the art results on English.
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REXEL: An end-to-end model for document-level relation extraction and entity linking

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By Nacime Bouziani, Shubhi Tyagi, Joseph Fisher
| Amazon Science
Extracting structured information from unstructured text is critical for many downstream NLP applications and is traditionally achieved by closed information extraction (cIE). However, existing approaches for cIE suffer from two limitations: (i) they are often pipelines which makes them prone to error propagation, and/or (ii) they are restricted to sentence level which prevents them from capturing long-range dependencies and results in expensive inference time.
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