Customer-obsessed science

Large language models can accumulate incorrect or outdated knowledge as the real world evolves. Compared to typical solutions such as retraining, retrieval augmented generation, model editing offers an effective yet low cost solution to address this issue. However, existing model editing algorithms employ manual selection of edit layers, which requires prior domain knowledge or expensive architecturespecific

Large language models (LLMs) have demonstrated impressive performance on a number of natural language processing tasks, such as question answering and text summarization. However, their performance on sequence labeling tasks, such as intent classification and slot filling (IC-SF), which is a central component in personal assistant systems, lags significantly behind discriminative models. Furthermore, there

Anomaly detection on graphs focuses on identifying irregular patterns or anomalous nodes within graph-structured data, which deviate significantly from the norm. This domain gains paramount importance due to its wide applicability in various fields such as spam detection, anti-money laundering, and network security. In the application of anomaly detection on graphs, tackling the challenges posed by label

Modern Automatic Speech Recognition (ASR) systems are evaluated with respect to Word Error Rate (WER). While WER is a useful metric for training and evaluation of speech models, it does not fully reflect the difference in semantics between predicted and ground truth transcriptions. In conversational voice assistants, the ability to sufficiently understand the semantic meaning of the user request is often

Designing performant and noise-robust circuits for Quantum Machine Learning (QML) is challenging — the design space scales exponentially with circuit size, and there are few well-supported guiding principles for QML circuit design. Although recent Quantum Circuit Search (QCS) methods attempt to search for performant QML circuits that are also robust to hardware noise, they directly adopt designs from classical