Search Results for author:
Found 10 papers, 2 papers with code
Position-Aware Parameter Efficient Fine-Tuning Approach for Reducing Positional Bias in LLMs
Recent advances in large language models (LLMs) have enhanced their ability to process long input contexts.
Human as AI Mentor: Enhanced Human-in-the-loop Reinforcement Learning for Safe and Efficient Autonomous Driving
In this paradigm, the human expert serves as a mentor to the AI agent.
KEPLET: Knowledge-Enhanced Pretrained Language Model with Topic Entity Awareness
In recent years, Pre-trained Language Models (PLMs) have shown their superiority by pre-training on unstructured text corpus and then fine-tuning on downstream tasks.
CLICKER: Attention-Based Cross-Lingual Commonsense Knowledge Transfer
Recent advances in cross-lingual commonsense reasoning (CSR) are facilitated by the development of multilingual pre-trained models (mPTMs).
Self-Aware Feedback-Based Self-Learning in Large-Scale Conversational AI
Self-learning paradigms in large-scale conversational AI agents tend to leverage user feedback in bridging between what they say and what they mean.
A Vocabulary-Free Multilingual Neural Tokenizer for End-to-End Task Learning
Additionally, the dependency on a fixed vocabulary limits the subword models' adaptability across languages and domains.
Query Expansion and Entity Weighting for Query Reformulation Retrieval in Voice Assistant Systems
However, these methods rarely focus on query expansion and entity weighting simultaneously, which may limit the scope and accuracy of the query reformulation retrieval.
Incremental user embedding modeling for personalized text classification
Individual user profiles and interaction histories play a significant role in providing customized experiences in real-world applications such as chatbots, social media, retail, and education.
VAE based Text Style Transfer with Pivot Words Enhancement Learning
Text Style Transfer (TST) aims to alter the underlying style of the source text to another specific style while keeping the same content.
LSTM-based Whisper Detection
We prove that, with enough data, the LSTM model is indeed as capable of learning whisper characteristics from LFBE features alone compared to a simpler MLP model that uses both LFBE and features engineered for separating whisper and normal speech.
