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Found 6 papers, 3 papers with code
Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations
We introduce Llama Guard, an LLM-based input-output safeguard model geared towards Human-AI conversation use cases.
Attention or Convolution: Transformer Encoders in Audio Language Models for Inference Efficiency
In this paper, we show that a simple self-supervised pre-trained audio model can achieve comparable inference efficiency to more complicated pre-trained models with speech transformer encoders.
Effective Long-Context Scaling of Foundation Models
We also examine the impact of various design choices in the pretraining process, including the data mix and the training curriculum of sequence lengths -- our ablation experiments suggest that having abundant long texts in the pretrain dataset is not the key to achieving strong performance, and we empirically verify that long context continual pretraining is more efficient and similarly effective compared to pretraining from scratch with long sequences.
Llama 2: Open Foundation and Fine-Tuned Chat Models
In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters.
Ranked #2 on
Question Answering
on PubChemQA
Structured Summarization: Unified Text Segmentation and Segment Labeling as a Generation Task
In this work, we propose a single encoder-decoder neural network that can handle long documents and conversations, trained simultaneously for both segmentation and segment labeling using only standard supervision.
Sieg at MEDIQA 2019: Multi-task Neural Ensemble for Biomedical Inference and Entailment
This paper presents a multi-task learning approach to natural language inference (NLI) and question entailment (RQE) in the biomedical domain.
