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20212026
most citedChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools

180 citations · 284 across the 30 of their papers we have counts for

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Showing 2023Show all

8 papers · 1 filter

cs.CV2023

Text-Image Conditioned Diffusion for Consistent Text-to-3D Generation

Yuze He, Yushi Bai, Matthieu Lin +5

By lifting the pre-trained 2D diffusion models into Neural Radiance Fields (NeRFs), text-to-3D generation methods have made great progress. Many state-of-the-art approaches usually…

cs.CL2023★ 3 cited

WaterBench: Towards Holistic Evaluation of Watermarks for Large Language Models

Shangqing Tu, Yuliang Sun, Yushi Bai +3

To mitigate the potential misuse of large language models (LLMs), recent research has developed watermarking algorithms, which restrict the generation process to leave an invisible…

cs.LG2023

Know2BIO: A Comprehensive Dual-View Benchmark for Evolving Biomedical Knowledge Graphs

Yijia Xiao, Dylan Steinecke, Alexander Russell Pelletier +3

Knowledge graphs (KGs) have emerged as a powerful framework for representing and integrating complex biomedical information. However, assembling KGs from diverse sources remains a…

cs.CV2023★ 12 cited

TBench: Benchmarking Current Progress in Text-to-3D Generation

Yuze He, Yushi Bai, Matthieu Lin +6

Recent methods in text-to-3D leverage powerful pretrained diffusion models to optimize NeRF. Notably, these methods are able to produce high-quality 3D scenes without training on 3…

cs.CL2023★ 3 cited

PrivacyMind: Large Language Models Can Be Contextual Privacy Protection Learners

Yijia Xiao, Yiqiao Jin, Yushi Bai +10

The proliferation of Large Language Models (LLMs) has driven considerable interest in fine-tuning them with domain-specific data to create specialized language models. Nevertheless…

cs.CL2023★ 10 cited

LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding

Yushi Bai, Xin Lv, Jiajie Zhang +10

Although large language models (LLMs) demonstrate impressive performance for many language tasks, most of them can only handle texts a few thousand tokens long, limiting their appl…

cs.CL2023★ 24 cited

KoLA: Carefully Benchmarking World Knowledge of Large Language Models

Jifan Yu, Xiaozhi Wang, Shangqing Tu +32

The unprecedented performance of large language models (LLMs) necessitates improvements in evaluations. Rather than merely exploring the breadth of LLM abilities, we believe meticu…

cs.CL2023★ 21 cited

Benchmarking Foundation Models with Language-Model-as-an-Examiner

Yushi Bai, Jiahao Ying, Yixin Cao +10

Numerous benchmarks have been established to assess the performance of foundation models on open-ended question answering, which serves as a comprehensive test of a model's ability…