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20192026
most citedA Survey on Evaluation of Large Language Models

202 citations · 328 across the 24 of their papers we have counts for

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Showing 2023 · cs.CLShow all

7 papers · 2 filters

cs.CL2023

TRAMS: Training-free Memory Selection for Long-range Language Modeling

Haofei Yu, Cunxiang Wang, Yue Zhang +1

The Transformer architecture is crucial for numerous AI models, but it still faces challenges in long-range language modeling. Though several specific transformer architectures hav…

cs.CL2023★ 55 cited

Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity

Cunxiang Wang, Xiaoze Liu, Yuanhao Yue +13

This survey addresses the crucial issue of factuality in Large Language Models (LLMs). As LLMs find applications across diverse domains, the reliability and accuracy of their outpu…

cs.CL2023★ 202 cited

A Survey on Evaluation of Large Language Models

Yupeng Chang, Xu Wang, Jindong Wang +13

Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications. As LLMs continue to…

cs.CL2023★ 29 cited

PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization

Yidong Wang, Zhuohao Yu, Zhengran Zeng +10

Instruction tuning large language models (LLMs) remains a challenging task, owing to the complexity of hyperparameter selection and the difficulty involved in evaluating the tuned…

cs.CL2023

Exploiting Abstract Meaning Representation for Open-Domain Question Answering

Cunxiang Wang, Zhikun Xu, Qipeng Guo +4

The Open-Domain Question Answering (ODQA) task involves retrieving and subsequently generating answers from fine-grained relevant passages within a database. Current systems levera…

cs.CL2023

RFiD: Towards Rational Fusion-in-Decoder for Open-Domain Question Answering

Cunxiang Wang, Haofei Yu, Yue Zhang

Open-Domain Question Answering (ODQA) systems necessitate a reader model capable of generating answers by simultaneously referring to multiple passages. Although representative mod…