Language relatedness evaluation for multilingual neural machine translation

Mi, Chenggang; Xie, Shaoliang

doi:10.1016/j.neucom.2023.127115

Cited by 3 publications

(2 citation statements)

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“…Many businesses already rely heavily on artificial intelligence, and it is rapidly being utilized to guide global policy and public sector choices [14]. In this regard, the results of this analysis recommend data collection methods,artificial decision makers,fitness functions, and data acquisitions as a tool of evaluation of large language models, linking language models, and learning systems, as well as artificial intelligence, decision-making, learning systems, and language processing.…”

Section: Figure 3 Frequetly Search Wordsmentioning

confidence: 99%

“…The study explores the effectiveness of Chatbots (LLMs) in improving L2 vocabulary learning, highlighting their positive impact on receptive and productive knowledge acquisition and incidental learning, particularly in language education. Oduoye et al, caution against the adoption of LLMs in medical writing because of the potential bias present in the model's creation, which could result in adverse consequences downstream [13][14].…”

Section: A Introductionmentioning

confidence: 99%

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Evaluations of Large Language Models a Bibliometric analysis

Sekwatlakwatla,

Malele

2024

ijcs

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The development of artificial intelligence (AI) and the increased curiosity about how large language models (LLMs) may maximize an organization's opportunities and the ethical implications of LLMs, such as the ability to generate human-like text, give rise to concerns regarding disinformation and fake news. As a result, it is crucial to develop evaluation benchmarks that take into account the social and ethical implications involved. The great challenges of LLMs lack awareness of their own limitations, yet they persist in producing responses to the best of their capabilities. This often results in seemingly plausible but ultimately incorrect answers, posing challenges to the implementation of reliable generative AI in industry. This paper aims to delve into the evaluation metrics of machine-learning models' performance, specifically focusing on LLM. Therefore, bibliometric analysis utilized to explore and analyze various techniques and methods used in evaluating large language models. Additionally, it sheds light on the specific areas of focus when evaluating these models. The results show that natural language processing systems, classification of information, and computational linguistics are some of the techniques used to evaluate large language models. This work paves the way for future investigations employing extensive language models.

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Section: Figure 3 Frequetly Search Wordsmentioning

confidence: 99%

Section: A Introductionmentioning

confidence: 99%