Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback
LLM-Augmenter significantly reduces ChatGPT’s hallucinations without sacrificing the fluency and informativeness of its responses.
Implement LLM-Augmenter to improve the performance of large language models in real-world mission-critical applications such as task-oriented dialog and question answering.
Decoupling Human and Camera Motion from Videos in the Wild
The optimization method proposed in the paper decouples the camera and human motion, allowing the placement of people in the same world coordinate frame.
Use the proposed optimization method to reconstruct global human trajectories from videos in challenging in-the-wild scenarios to improve performance of downstream tracking in PoseTrack.
Language-Driven Representation Learning for Robotics
Voltron's language-driven representations strictly outperform the prior art.
Implement Voltron to learn from human videos and associated captions for language-conditioned imitation learning and intent scoring for human-robot collaboration among other diverse set of robot learning problems.
Modulating Pretrained Diffusion Models for Multimodal Image Synthesis
Enables conditional image synthesis using pretrained models with multimodal conditioning modules (MCM)
Enables user control over spatial layout of images and better alignment with conditioning inputs. Cheap to train with limited examples.
MUX-PLMs: Pre-training Language Models with Data Multiplexing
Pre-trained multiplexed language models (MUX-PLMs) for improving inference efficiency in downstream tasks
Achieves 2x/5x inference speedup with minimal drop in performance on GLUE and token-level tasks. Released pre-trained checkpoints for different configurations.