Papers by Anthony B. Sicilia
Contextual ASR Error Handling with LLMs Augmentation for Goal-Oriented Conversational AI (2025.coling-industry)
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Yuya Asano, Sabit Hassan, Paras Sharma, Anthony B. Sicilia, Katherine Atwell, Diane Litman, Malihe Alikhani
| Challenge: | Existing ASR correction methods rely on prior user data or named entities . Existing methods based on prior data are not available for goal-oriented dialogues . |
| Approach: | They propose a method that integrates contextual information from the dialogue states of a goal-oriented conversational AI and its tasks into a large language model. |
| Outcome: | The proposed method improves recall and F1 of correction by 34% and 16% while maintaining precision and false positive rate. |
Combining Discourse Coherence with Large Language Models for More Inclusive, Equitable, and Robust Task-Oriented Dialogue (2024.lrec-main)
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| Challenge: | Large language models (LLMs) are capable of generating well-formed responses, but they struggle in goal-oriented settings. |
| Approach: | They propose a discourse-aware multimodal task-oriented dialogue system that combines discourse theories with offline LLM generation. |
| Outcome: | The proposed system reduces misunderstandings in the dialect of African-American Vernacular English from 93% to 57%. |
An Active Learning Framework for Inclusive Generation by Large Language Models (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) exhibit bias toward underrepresented groups, despite advances in active learning. |
| Approach: | They propose a clustering-based active learning framework enhanced with knowledge distillation that transforms the intermediate outputs of the learner model to yield more representative models without prior knowledge of underlying data distribution. |
| Outcome: | The proposed framework improves performance across data subgroups and lexical diversity, underscoring the model’s resilience to skewness in available data. |