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Optimizing Enterprise Conversational AI: Accelerating Response Accuracy with Custom Dataset fine-tuning

Intelligent Information Management 2024年第2期16卷 65-76页

作者： Yash Kishore Independent Researcher Austin USA

As the realm of enterprise-level conversational AI continues to evolve, it becomes evident that while generalized Large Language Models (LLMs) like GPT-3.5 bring remarkable capabilities, they also bring forth formidable challenges. These models, honed on vast and diverse datasets, have undoubtedly pushed the boundaries of natural language understanding and generation. However, they often stumble when faced with the intricate demands of nuanced enterprise applications. This research advocates for a strategic paradigm shift, urging enterprises to embrace a fine-tuning approach as a means to optimize conversational AI. While generalized LLMs are linguistic marvels, their inability to cater to the specific needs of businesses across various industries poses a critical challenge. This strategic shift involves empowering enterprises to seamlessly integrate their own datasets into LLMs, a process that extends beyond linguistic enhancement. The core concept of this approach centers on customization, enabling businesses to fine-tune the AI’s functionality to fit precisely within their unique business landscapes. By immersing the LLM in industry-specific documents, customer interaction records, internal reports, and regulatory guidelines, the AI transcends its generic capabilities to become a sophisticated conversational partner aligned with the intricacies of the enterprise’s domain. The transformative potential of this fine-tuning approach cannot be overstated. It enables a transition from a universal AI solution to a highly customizable tool. The AI evolves from being a linguistic powerhouse to a contextually aware, industry-savvy assistant. As a result, it not only responds with linguistic accuracy but also with depth, relevance, and resonance, significantly elevating user experiences and operational efficiency. In the subsequent sections, this paper delves into the intricacies of fine-tuning, exploring the multifaceted challenges and abundant opportunities it presents. It addresses the technical intricacies of data in

关键词： fine-tuning Dataset AI Conversational Enterprise LLM

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Regulation of insect behavior by non-coding RNAs

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Science China(Life Sciences) 2024年第6期67卷 1106-1118页

作者： Jing He Le Kang State Key Laboratory of Integrated Management of Pest Insects and Rodents Institute of ZoologyChinese Academy of SciencesBeijing 100101China Beijing Institutes of Life Sciences Chinese Academy of SciencesBeijing 100101China College of Life Science Hebei UniversityBaoding 071002China

The adaptation of insects to environments relies on a sophisticated set of behaviors controlled by molecular and physiological processes.Over the past several decades,accumulating studies have unveiled the roles of non-coding RNAs(nc RNAs)in regulating insect behaviors.nc RNAs assume particularly pivotal roles in the behavioral plasticity of insects by rapidly responding to environmental stimuli.nc RNAs also contribute to the maintenance of homeostasis of insects by fine-tuning the expression of target genes.However,a comprehensive review of nc RNAs'roles in regulating insect behaviors has yet to be conducted.Here,we present the recent progress in our understanding of how nc RNAs regulate various insect behaviors,including flight and movement,social behavior,reproduction,learning and memory,and feeding.We refine the intricate mechanisms by which nc RNAs modulate the function of neural,motor,reproductive,and other physiological systems,as well as gene expression in insects like fruit flies,social insects,locusts,and mosquitos.Furthermore,we discuss potential avenues for future studies in nc RNA-mediated insect behaviors.

关键词： ncRNAs insect behavior plasticity fine-tuning environmental adaptation

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Classification of Conversational Sentences Using an Ensemble Pre-Trained Language Model with the fine-Tuned Parameter

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Computers, Materials & Continua 2024年第2期78卷 1669-1686页

作者： R.Sujatha K.Nimala Department of Networking and Communications SRM Institute of Science and TechnologyKattankulathurChengalpattuTamilnadu603203India

Sentence classification is the process of categorizing a sentence based on the context of the sentence.Sentence categorization requires more semantic highlights than other tasks,such as dependence parsing,which requires more syntactic elements.Most existing strategies focus on the general semantics of a conversation without involving the context of the sentence,recognizing the progress and comparing impacts.An ensemble pre-trained language model was taken up here to classify the conversation sentences from the conversation corpus.The conversational sentences are classified into four categories:information,question,directive,and commission.These classification label sequences are for analyzing the conversation progress and predicting the pecking order of the conversation.Ensemble of Bidirectional Encoder for Representation of Transformer(BERT),Robustly Optimized BERT pretraining Approach(RoBERTa),Generative Pre-Trained Transformer(GPT),DistilBERT and Generalized Autoregressive Pretraining for Language Understanding(XLNet)models are trained on conversation corpus with hyperparameters.Hyperparameter tuning approach is carried out for better performance on sentence classification.This Ensemble of Pre-trained Language Models with a Hyperparameter tuning(EPLM-HT)system is trained on an annotated conversation dataset.The proposed approach outperformed compared to the base BERT,GPT,DistilBERT and XLNet transformer models.The proposed ensemble model with the fine-tuned parameters achieved an F1_score of 0.88.

关键词： Bidirectional encoder for representation of transformer conversation ensemble model fine-tuning generalized autoregressive pretraining for language understanding generative pre-trained transformer hyperparameter tuning natural language processing robustly optimized BERT pretraining approach sentence classification transformer models

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Improving BERT fine-tuning via Self-Ensemble and Self-Distillation

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Journal of Computer Science & Technology 2023年第4期38卷 853-866页

作者：许一格邱锡鹏周浬皋黄萱菁 School of Computer Science Fudan UniversityShanghai 200433China Shanghai Key Laboratory of Intelligent Information Processing Fudan UniversityShanghai 200433China Huawei Technologies Co. Ltd.Hangzhou 310052China

fine-tuning pre-trained language models like BERT have become an effective way in natural language processing(NLP)and yield state-of-the-art results on many downstream tasks.Recent studies on adapting BERT to new tasks mainly focus on modifying the model structure,re-designing the pre-training tasks,and leveraging external data and knowledge.The fine-tuning strategy itself has yet to be fully explored.In this paper,we improve the fine-tuning of BERT with two effective mechanisms:self-ensemble and self-distillation.The self-ensemble mechanism utilizes the checkpoints from an experience pool to integrate the teacher model.In order to transfer knowledge from the teacher model to the student model efficiently,we further use knowledge distillation,which is called self-distillation because the distillation comes from the model itself through the time dimension.Experiments on the GLUE benchmark and the Text Classification benchmark show that our proposed approach can significantly improve the adaption of BERT without any external data or knowledge.We conduct exhaustive experiments to investigate the efficiency of the self-ensemble and self-distillation mechanisms,and our proposed approach achieves a new state-of-the-art result on the SNLI dataset.

关键词： BERT deep learning fine-tuning natural language processing(NLP) pre-training model

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Mining and fine-tuning sugar uptake system for titer improvement of milbemycins in Streptomyces bingchenggensis

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Synthetic and Systems Biotechnology 2020年第3期5卷 214-221页

作者： Pinjiao Jin Shanshan Li Yanyan Zhang Liyang Chu Hairong He Zhuoxu Dong Wensheng Xiang School of Life Science Northeast Agricultural UniversityNo.59 Mucai StreetXiangfang DistrictHarbin150030China State Key Laboratory for Biology of Plant Diseases and Insect Pests Institute of Plant ProtectionChinese Academy of Agricultural SciencesBeijing100193China College of Pharmacy Henan University of Chinese MedicineZhengzhou 450046HenanChina

Dramatic decrease of sugar uptake is a general phenomenon in Streptomyces at stationary phase,when antibiotics are extensively produced.Milbemycins produced by Streptomyces bingchenggensis are a group of valuable macrolide biopesticides,while the low yield and titer impede their broad applications in agricultural field.Considering that inadequate sugar uptake generally hinders titer improvement of desired products,we mined the underlying sugar uptake systems and fine-tuned their expression in this work.First,we screened the candidates at both genomic and transcriptomic level in S.bingchenggensis.Then,two ATP-binding cassette transporters named TP2 and TP5 were characterized to improve milbemycin titer and yield significantly.Next,the appropriate native temporal promoters were selected and used to tune the expression of TP2 and TP5,resulting in a maximal milbemycin A3/A4 titer increase by 36.9%to 3321 mg/L.Finally,TP2 and TP5 were broadly finetuned in another two macrolide biopesticide producers Streptomyces avermitilis and Streptomyces cyaneogriseus,leading to a maximal titer improvement of 34.1%and 52.6%for avermectin B1a and nemadectin,respectively.This work provides useful transporter tools and corresponding engineering strategy for Streptomyces.

关键词： Streptomyces Sugar uptake system fine-tuning Titer improvement Milbemycins Macrolide biopesticides

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Ensemble Making Few-Shot Learning Stronger

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Data Intelligence 2022年第3期4卷 529-551页

作者： Qiang Lin Yongbin Liu Wen Wen Zhihua Tao Chunping Ouyang Yaping Wan Computer School University of South China 421001China Hunan provincial base for scientific and technological innovation cooperation HunanChina

Few-shot learning has been proposed and rapidly emerging as a viable means for completing various tasks.Many few-shot models have been widely used for relation learning tasks.However,each of these models has a shortage of capturing a certain aspect of semantic features,for example,CNN on long-range dependencies part,Transformer on local features.It is difficult for a single model to adapt to various relation learning,which results in a high variance problem.Ensemble strategy could be competitive in improving the accuracy of few-shot relation extraction and mitigating high variance risks.This paper explores an ensemble approach to reduce the variance and introduces fine-tuning and feature attention strategies to calibrate relation-level features.Results on several few-shot relation learning tasks show that our model significantly outperforms the previous state-of-the-art models.

关键词： Few-shot learning Relation extraction Ensemble learning Attention Mechanism fine-tuning

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Model-independent Veltman condition,naturalness and the little hierarchy problem

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Chinese Physics C 2022年第1期46卷 31-42页

作者： Fayez Abu-Ajamieh LUPM UMR5299 Universitéde Montpellier34095 MontpellierFrance

We adopt a bottom-up Effective Field Theory(EFT)approach to derive a model-independent Veltman condition to cancel out the quadratic divergences in the Higgs mass.We show using the equivalence theorem that all the deviations in the Higgs couplings to the W and Z from the SM predictions should vanish.We argue based on tree-level unitarity that any new physics that naturally cancels out the quadratic divergences should be ≤19 TeV.We show that the level of fine-tuning required is unless the O(0:1%-1%) UV sector has a symmetry that forces the satisfaction of the model-independent Veltman condition,in which case all fine-tuning is eliminated.We also conjecture that,if no new physics that couples to the Higgs is observed up to~19 TeV,or if the Higgs couplings to the SM particles conform to the SM predictions,then the Higgs either does not couple to any UV sector or is fine-tuned.

关键词： effective field theory Higgs little hierarchy problem quadratic divergence fine-tuning unitarity byeond the standard model

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A multiple-pass ring oscillator based dual-loop phase-locked loop

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Journal of Semiconductors 2009年第10期30卷 132-136页

作者：陈丹凤任俊彦邓晶晶李巍李宁 State Key Laboratory of ASIC & System Fudan University

A dual-loop phase-locked loop（PLL）for wideband operation is proposed.The dual-loop architecture combines a coarse-tuning loop with a fine-tuning one,enabling a wide tuning range and low voltage-controlled oscillator（VCO）gain without poisoning phase noise and reference spur suppression performance.An analysis of the phase noise and reference spur of the dual-loop PLL is emphasized.A novel multiple-pass ring VCO is designed for the dual-loop application.It utilizes both voltage-control and current-control simultaneously in the delay cell. The PLL is fabricated in Jazz 0.18-μm RF CMOS technology.The measured tuning range is from 4.2 to 5.9 GHz.It achieves a low phase noise of–99 dBc/Hz@1 MHz offset from a 5.5 GHz carrier.

关键词： coarse-tuning dual-loop fine-tuning phase-locked loop phase noise

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Medical care service fee makes the value of doctor＇s work recognized

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Chinese Medical Journal 2014年第13期127卷 2556-2556页

作者： Zhou Haijiang Lan Tianfei Yan Yong Li Chunsheng Capital Med Univ Beijing Chao Yang Hosp Dept Emergency Med Beijing 100020 Peoples R China Chengde Med Coll Affiliated Hosp Dept Dermatol Chengde 067000 Hebei Peoples R China Capital Med Univ Beijing Shijitan Hosp Dept Urol Beijing 100038 Peoples R China

To the editor： Beijing has begun implementing its new public hospital reform programs since July 1, 2012.1 According to the new policies, patients are charged a medical care service fee which varies according to doctor＇s seniority while the 15 percent of drug markup is eliminated, aiming at reducing hospitals＇ dependency on profits from drug prescriptions and fine-tuning the structure of hospitals＇ incomes.

关键词： doctor's work recognized fine-tuning

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BERT-TECNN模型的文本分类方法研究

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计算机工程与应用 2021年第18期57卷 186-193页

作者：李铁飞生龙吴迪河北工程大学信息与电气工程学院河北邯郸056107 河北工程大学河北省安防信息感知与处理重点实验室河北邯郸056107

由于Bert-base,Chinese预训练模型参数巨大,在做分类任务微调时内部参数变化较小,易产生过拟合现象,泛化能力弱,且该模型是以字为单位进行的预训练,包含词信息量较少。针对这些问题,提出了BERT-TECNN模型,模型使用Bert-base,Chinese模... 详细信息

由于Bert-base,Chinese预训练模型参数巨大,在做分类任务微调时内部参数变化较小,易产生过拟合现象,泛化能力弱,且该模型是以字为单位进行的预训练,包含词信息量较少。针对这些问题,提出了BERT-TECNN模型,模型使用Bert-base,Chinese模型作为动态字向量模型,输出包含深度特征信息的字向量,Transformerencoder层再次对数据进行多头自注意力计算,提取特征信息,以提高模型的泛化能力,CNN层利用不同大小卷积核,捕捉每条数据中不同长度词的信息,最后应用softmax进行分类。该模型与Word2Vec+CNN、Word2Vec+BiLSTM、Elmo+CNN、BERT+CNN、BERT+BiLSTM、BERT+Transformer等深度学习文本分类模型在三种数据集上进行对比实验,得到的准确率、精确率、召回率、F1测度值均为最高。实验表明该模型有效地提取了文本中字词的特征信息,优化了过拟合问题,提高了泛化能力。

关键词： bert transformer encoder CNN 文本分类 fine-tuning self-attention 过拟合

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