Search Results for author:
Found 9 papers, 3 papers with code
CoDi: Conversational Distillation for Grounded Question Answering
This is a typical on-device scenario for specialist SLMs, allowing for open-domain model responses, without requiring the model to "memorize" world knowledge in its limited weights.
PRoDeliberation: Parallel Robust Deliberation for End-to-End Spoken Language Understanding
Spoken Language Understanding (SLU) is a critical component of voice assistants; it consists of converting speech to semantic parses for task execution.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
PrE-Text: Training Language Models on Private Federated Data in the Age of LLMs
Altogether, these results suggest that training on DP synthetic data can be a better option than training a model on-device on private distributed data.
Large Language Models as Zero-shot Dialogue State Tracker through Function Calling
We also show that by fine-tuning on a small collection of diverse task-oriented dialogues, we can equip modestly sized models, specifically a 13B parameter LLaMA2-Chat model, with function-calling capabilities and DST performance comparable to ChatGPT while maintaining their chat capabilities.
Data-Efficiency with a Single GPU: An Exploration of Transfer Methods for Small Language Models
Multi-task learning (MTL), instruction tuning, and prompting have recently been shown to improve the generalizability of large language models to new tasks.
STOP: A dataset for Spoken Task Oriented Semantic Parsing
Furthermore, in addition to the human-recorded audio, we are releasing a TTS-generated version to benchmark the performance for low-resource domain adaptation of end-to-end SLU systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
RETRONLU: Retrieval Augmented Task-Oriented Semantic Parsing
While large pre-trained language models accumulate a lot of knowledge in their parameters, it has been demonstrated that augmenting it with non-parametric retrieval-based memory has a number of benefits from accuracy improvements to data efficiency for knowledge-focused tasks, such as question answering.
Lattice-based Improvements for Voice Triggering Using Graph Neural Networks
A pure trigger-phrase detector model doesn't fully utilize the intent of the user speech whereas by using the complete decoding lattice of user audio, we can effectively mitigate speech not intended for the smart assistant.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Active Learning for Domain Classification in a Commercial Spoken Personal Assistant
We describe a method for selecting relevant new training data for the LSTM-based domain selection component of our personal assistant system.
