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FP6-LLM: Efficiently Serving Large Language Models Through FP6-Centric Algori...
Six-bit quantization can effectively reduce the size of large language models and preserve the model quality consistently across varied applications. -
Fairness Certification for Natural Language Processing and Large Language Models
The dataset used in the paper is a large corpus of text data, which is used to train and evaluate natural language processing models. -
Integer or floating point? new outlooks for low-bit quantization on large lan...
The dataset used in the paper is not explicitly described, but it is mentioned that it is a large language model dataset. -
A comprehensive study on post-training quantization for large language models
The ZeroQuant dataset is a large language model dataset used in the paper. -
Opt: Open pre-trained transformer language models
The OPT dataset is a large language model dataset used in the paper. -
ZeroQuant-FP: A Leap Forward in LLMs Post-Training W4A8 Quantization Using Fl...
The dataset used in the paper is not explicitly described, but it is mentioned that it is a large language model dataset. -
PRIVACY-PRESERVING IN-CONTEXT LEARNING FOR LARGE LANGUAGE MODELS
In-context learning (ICL) is an important capability of Large Language Models (LLMs), enabling these models to dynamically adapt based on specific, in-context exemplars, thereby... -
COMMUNITY-CROSS-INSTRUCT
COMMUNITY-CROSS-INSTRUCT: Unsupervised Instruction Generation for Aligning Large Language Models to Online Communities -
Evaluating large language models trained on code
The paper presents the results of the OpenAI Codex evaluation on generating Python code. -
WikiText-2 dataset
The WikiText-2 dataset is a benchmark for evaluating the performance of large language models. -
C4 dataset
The dataset used in the paper is not explicitly mentioned, but it is mentioned that the authors trained a GPT2 transformer language model on the C4 dataset. -
APTQ: Attention-aware Post-Training Mixed-Precision Quantization for Large La...
Large Language Models (LLMs) have greatly advanced the natural language processing paradigm. However, the high computational load and huge model sizes pose a grand challenge for... -
Latent Distance Guided Alignment Training for Large Language Models
Ensuring alignment with human preferences is a crucial characteristic of large language models (LLMs). Presently, the primary alignment methods, RLHF and DPO, require extensive... -
Llama: Open and efficient foundation language models
The LLaMA dataset is a large language model dataset used in the paper. -
OPT-66B and Llama2-70B
The dataset used in the paper is OPT-66B, a large language model, and Llama2-70B, another large language model. -
How do large language models capture the ever-changing world knowledge?
This paper presents a review of recent advances in large language models' ability to capture ever-changing world knowledge.
