Search Results for author:
Found 4 papers, 2 papers with code
Can LLMs get help from other LLMs without revealing private information?
In this work, we show the feasibility of applying cascade systems in such setups by equipping the local model with privacy-preserving techniques that reduce the risk of leaking private information when querying the remote model.
ScreenAI: A Vision-Language Model for UI and Infographics Understanding
At the heart of this mixture is a novel screen annotation task in which the model has to identify the type and location of UI elements.
Ranked #3 on
Visual Question Answering (VQA)
on InfographicVQA
(using extra training data)
LLMs cannot find reasoning errors, but can correct them!
While self-correction has shown promise in improving LLM outputs in terms of style and quality (e. g. Chen et al., 2023; Madaan et al., 2023), recent attempts to self-correct logical or reasoning errors often cause correct answers to become incorrect, resulting in worse performances overall (Huang et al., 2023).
Replacing Human Audio with Synthetic Audio for On-device Unspoken Punctuation Prediction
We present a novel multi-modal unspoken punctuation prediction system for the English language which combines acoustic and text features.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
