Srujana Merugu

1 month ago | amazon.science | Prasenjit Dey |Srujana Merugu |Varun Kumar

Large Language Models (LLMs) are known to hallucinate and generate non-factual outputs which can undermine user trust. Traditional methods to directly mitigate hallucinations, such as representation editing and contrastive decoding, often require additional training data and involve high implementation complexity. While ensemble-based approaches harness multiple LLMs to tap into the "wisdom of crowds", these methods overlook uncertainties in individual model responses.

Nov 16, 2023 | amazon.science | Prasenjit Dey |Srujana Merugu |Sankalp Dayal

Ordinal classification (OC), i.e., labeling instances along classes with a natural ordering, is common in multiple applications such as disease severity labeling and size or budget based recommendations. Often in practical scenarios, it is desirable to obtain a small set of likely classes with a guaranteed high chance of including the true class.