While large language models have proven effective in a huge range of downstream applications, they often generate text that is problematic or lacks a desired attribute. In this paper, we introduce Reward-Augmented Decoding (RAD), a text generation procedure that uses a small unidirectional reward model to encourage a language model... [Read More]

The Internet contains a wealth of knowledge – from the birthdays of historical figures to tutorials on how to code – all of which may be learned by language models. However, while certain pieces of information are ubiquitous on the web, others appear extremely rarely. In this paper, we study... [Read More]

• Benchmarked various Learning from Human Feedback methods and studied their overoptimization problem
• Introduced an efficient weighted decoding method that aligns text to a given attribute with uni-directional reward model
• Explored language models’ knowledge-learning process and their QA performance relative to their pre-training data
• Analyzed language model hallucination and tracked wrong answers in training corpus

• Built a Horizonte Service for Local Landing Page which displays local products available for pick up
• Deployed the service to production and verified its reliability with production data
• Onboarded downstream dependencies to fetch data and extended JSP to render user interface
• Configured shopping portal page type and added routing rules from amazon.com

• Trained Encoder-Decoder LSTM for anomaly detection on time series data.
• Participated in the design of Control Chart and Anomaly Detection Module.
• Performed model tuning and data grouping which improved f1 score from 0.41 to 0.48.