Search Results for author:
Found 18 papers, 12 papers with code
K-Sort Arena: Efficient and Reliable Benchmarking for Generative Models via K-wise Human Preferences
Arena platform, which gathers user votes on model comparisons, can rank models with human preferences.
Incorporating Lexical and Syntactic Knowledge for Unsupervised Cross-Lingual Transfer
In this paper, we present a novel framework called "Lexicon-Syntax Enhanced Multilingual BERT" that combines both lexical and syntactic knowledge.
Online Training of Large Language Models: Learn while chatting
Large Language Models(LLMs) have dramatically revolutionized the field of Natural Language Processing(NLP), offering remarkable capabilities that have garnered widespread usage.
ALLaVA: Harnessing GPT4V-Synthesized Data for Lite Vision-Language Models
Large vision-language models (LVLMs) have shown premise in a broad range of vision-language tasks with their strong reasoning and generalization capabilities.
MLLM-Bench: Evaluating Multimodal LLMs with Per-sample Criteria
To this end, in our paper, we propose a new evaluation paradigm for MLLMs, which is evaluating MLLMs with per-sample criteria using potent MLLM as the judge.
HuatuoGPT-II, One-stage Training for Medical Adaption of LLMs
We validate the new protocol in the domains where proprietary LLMs like ChatGPT perform relatively poorly, such as Traditional Chinese Medicine.
AceGPT, Localizing Large Language Models in Arabic
This paper is devoted to the development of a localized Large Language Model (LLM) specifically for Arabic, a language imbued with unique cultural characteristics inadequately addressed by current mainstream models.
CMB: A Comprehensive Medical Benchmark in Chinese
We hope this benchmark provide first-hand experience in existing LLMs for medicine and also facilitate the widespread adoption and enhancement of medical LLMs within China.
HuatuoGPT, towards Taming Language Model to Be a Doctor
Experimental results demonstrate that HuatuoGPT achieves state-of-the-art results in performing medical consultation among open-source LLMs in GPT-4 evaluation, human evaluation, and medical benchmark datasets.
Huatuo-26M, a Large-scale Chinese Medical QA Dataset
Moreover, we also experimentally show the benefit of the proposed dataset in many aspects: (i) trained models for other QA datasets in a zero-shot fashion; and (ii) as external knowledge for retrieval-augmented generation (RAG); and (iii) improving existing pre-trained language models by using the QA pairs as a pre-training corpus in continued training manner.
Phoenix: Democratizing ChatGPT across Languages
This paper presents our efforts to democratize ChatGPT across language.
Effective Open Intent Classification with K-center Contrastive Learning and Adjustable Decision Boundary
In this paper, we introduce novel K-center contrastive learning and adjustable decision boundary learning (CLAB) to improve the effectiveness of open intent classification.
Can Language Models Make Fun? A Case Study in Chinese Comical Crosstalk
However, the humor aspect of natural language is relatively under-investigated, especially in the age of pre-trained language models.
BERT-EMD: Many-to-Many Layer Mapping for BERT Compression with Earth Mover's Distance
In this way, our model can learn from different teacher layers adaptively for various NLP tasks.
Empirical Evaluation of Multi-task Learning in Deep Neural Networks for Natural Language Processing
Multi-Task Learning (MTL) aims at boosting the overall performance of each individual task by leveraging useful information contained in multiple related tasks.
Multi-loss-aware Channel Pruning of Deep Networks
Particularly, we introduce an additional loss to encode the differences in the feature and semantic distributions within feature maps between the baseline model and the pruned one.
Cluster Regularized Quantization for Deep Networks Compression
Deep neural networks (DNNs) have achieved great success in a wide range of computer vision areas, but the applications to mobile devices is limited due to their high storage and computational cost.
A novel channel pruning method for deep neural network compression
In order to accelerate the selection process, the proposed method formulates it as a search problem, which can be solved efficiently by genetic algorithm.
