Search Results for author:
Found 10 papers, 6 papers with code
BMInf: An Efficient Toolkit for Big Model Inference and Tuning
In recent years, large-scale pre-trained language models (PLMs) containing billions of parameters have achieved promising results on various NLP tasks.
MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies
For data scaling, we introduce a Warmup-Stable-Decay (WSD) learning rate scheduler (LRS), conducive to continuous training and domain adaptation.
D-Bot: Database Diagnosis System using Large Language Models
Database administrators (DBAs) play an important role in managing, maintaining and optimizing database systems.
Predicting Emergent Abilities with Infinite Resolution Evaluation
With PassUntil, we conduct a quantitative investigation into the scaling law of task performance.
QASnowball: An Iterative Bootstrapping Framework for High-Quality Question-Answering Data Generation
Recent years have witnessed the success of question answering (QA), especially its potential to be a foundation paradigm for tackling diverse NLP tasks.
CUGE: A Chinese Language Understanding and Generation Evaluation Benchmark
We argue that for general-purpose language intelligence evaluation, the benchmark itself needs to be comprehensive and systematic.
CPM-2: Large-scale Cost-effective Pre-trained Language Models
We present a suite of cost-effective techniques for the use of PLMs to deal with the efficiency issues of pre-training, fine-tuning, and inference.
CPM: A Large-scale Generative Chinese Pre-trained Language Model
However, applying GPT-3 to address Chinese NLP tasks is still challenging, as the training corpus of GPT-3 is primarily English, and the parameters are not publicly available.
OpenAttack: An Open-source Textual Adversarial Attack Toolkit
Textual adversarial attacking has received wide and increasing attention in recent years.
Adversarial Language Games for Advanced Natural Language Intelligence
In this work, we propose a challenging adversarial language game called Adversarial Taboo as an example, in which an attacker and a defender compete around a target word.
