Papers by Prathap Ramachandra
Error Detection in Large-Scale Natural Language Understanding Systems Using Transformer Models (2021.findings-acl)
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| Challenge: | Large-scale conversational assistants process every utterance using multiple models for domain, intent and named entity recognition. |
| Approach: | They combine utterance encodings from a RoBERTa model with the Nbest hypothesis produced by the production system to detect domain classification errors. |
| Outcome: | The proposed approach outperforms bi-LSTM models and a standalone model by 2.2% to 32.2% by ensembling multiple models. |
RS-DPO: A Hybrid Rejection Sampling and Direct Preference Optimization Method for Alignment of Large Language Models (2024.findings-naacl)
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| Challenge: | Reinforcement learning with human feedback (RLHF) is widely employed to align large language models with user intent. |
| Approach: | They propose to combine rejection sampling and direct preference optimization to improve alignment with user intent by identifying pairs of contrastive samples from human annotator and alternative LLMs. |
| Outcome: | The proposed method outperforms existing methods including RS, PPO, and DPO in a limited resource environment. |