Search Results for author: Jihan Yin

Found 4 papers, 2 papers with code

Detecting and Preventing Hallucinations in Large Vision Language Models

1 code implementation11 Aug 2023 Anisha Gunjal, Jihan Yin, Erhan Bas

We find that even the current state-of-the-art LVLMs (InstructBLIP) still contain a staggering 30 percent of the hallucinatory text in the form of non-existent objects, unfaithful descriptions, and inaccurate relationships.

Hallucination Question Answering +1

Empirical Analysis of the Strengths and Weaknesses of PEFT Techniques for LLMs

no code implementations28 Apr 2023 George Pu, Anirudh Jain, Jihan Yin, Russell Kaplan

As foundation models continue to exponentially scale in size, efficient methods of adaptation become increasingly critical.

NBDT: Neural-Backed Decision Tree

no code implementations ICLR 2021 Alvin Wan, Lisa Dunlap, Daniel Ho, Jihan Yin, Scott Lee, Suzanne Petryk, Sarah Adel Bargal, Joseph E. Gonzalez

Machine learning applications such as finance and medicine demand accurate and justifiable predictions, barring most deep learning methods from use.

NBDT: Neural-Backed Decision Trees

2 code implementations1 Apr 2020 Alvin Wan, Lisa Dunlap, Daniel Ho, Jihan Yin, Scott Lee, Henry Jin, Suzanne Petryk, Sarah Adel Bargal, Joseph E. Gonzalez

Machine learning applications such as finance and medicine demand accurate and justifiable predictions, barring most deep learning methods from use.

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