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Akademický článek

An experimental and kinetic modeling study of the auto-ignition delay times of trimethyl phosphate-in-air mixtures

Autor: Frederick Nii Ofei Bruce, Ruining He, Ren Xuan, Bai Xin, Yue Ma, Yang Li

Publikováno v: Applications in Energy and Combustion Science, Vol 17, Iss , Pp 100237- (2024)

Organophosphorus compounds (OPCs) are known to be combustion inhibitors (CI), fire suppressants, or flame retardant molecules (FRMs) for polymers and as surrogates (simulants) for the disposal or thermal degradation of chemical war agents (CWAs). Des

Externí odkaz: https://doaj.org/article/4170a32629b34c49b4c590ae0a9036d1

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I Learn Better If You Speak My Language: Understanding the Superior Performance of Fine-Tuning Large Language Models with LLM-Generated Responses

Autor: Ren, Xuan, Wu, Biao, Liu, Lingqiao

This paper explores an intriguing observation: fine-tuning a large language model (LLM) with responses generated by a LLM often yields better results than using responses generated by humans, particularly in reasoning tasks. We conduct an in-depth in

Externí odkaz: http://arxiv.org/abs/2402.11192

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Akademický článek

The role of urban green spaces in supporting active and healthy ageing: an exploration of behaviour–physical setting–gender correlations

Autor: Ye, Xi, Ren, Xuan, Shang, Yuanzheng, Liu, Jiayu, Feng, Huangyu, Zhang, Yun

Publikováno v: Archnet-IJAR: International Journal of Architectural Research, 2023, Vol. 18, Issue 4, pp. 775-795.

Externí odkaz: http://www.emeraldinsight.com/doi/10.1108/ARCH-04-2023-0096

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You Can Generate It Again: Data-to-text Generation with Verification and Correction Prompting

Autor: Ren, Xuan, Liu, Lingqiao

Despite significant advancements in existing models, generating text descriptions from structured data input, known as data-to-text generation, remains a challenging task. In this paper, we propose a novel approach that goes beyond traditional one-sh

Externí odkaz: http://arxiv.org/abs/2306.15933

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Out-of-Distribution Generalization in Text Classification: Past, Present, and Future

Autor: Yang, Linyi, Song, Yaoxiao, Ren, Xuan, Lyu, Chenyang, Wang, Yidong, Liu, Lingqiao, Wang, Jindong, Foster, Jennifer, Zhang, Yue

Machine learning (ML) systems in natural language processing (NLP) face significant challenges in generalizing to out-of-distribution (OOD) data, where the test distribution differs from the training data distribution. This poses important questions

Externí odkaz: http://arxiv.org/abs/2305.14104

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