Optimizing Enterprise Conversational AI: Accelerating Response Accuracy with Custom Dataset Fine-Tuning

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 integration, ethical considerations surrounding data usage, and the broader implications for the future of enterprise AI. The journey embarked upon in this research holds the potential to redefine the role of conversational AI in enterprises, ushering in an era where AI becomes a dynamic, deeply relevant, and highly effective tool, empowering businesses to excel in an ever-evolving digital landscape.

Language Assessment Feedback Towards Pedagogical Conversational Agent

Continuous development of technology provides an opportunity to incorporate feedback in online assessments.The mode of online instruction during the pandemic was the most significant survival change.Technology enabled every teacher and student to enter a virtual classroom to make sense of education.Feedback is part of language instruction and is a powerful key to improving students’learning performance.Feedback plays an influential and crucial role in teaching and learning.Feedback is an invaluable,ultimate learning tool for learners that aids them in not committing the same error again and creates impetus.Thus,knowing about formative exam feedback is students’right because quality feedback allures them.Given students’eagerness,providing feedback is considered a good practice to be followed by all the teaching faculty.Apropos of online feedback,the present study attempts to study how pedagogical agents provide online feedback in language assessments.The study also considers the characteristics of pedagogical conversational agents that are suitable for providing feedback in online language assessment.Simply put,the study encapsulates that screen agents play an essential role in students’motivation and acceptability of learning through feedback.

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

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.

Contrastive Analysis of Conversational Implicature between English and Chinese

Implicature is a universal feature of human communication.In conversations,it is common to see examples of something unsaid but communicated.After introducing the theory of conversational impilcature and its four maxims,this paper presents English and Chinese examples to compare their conversational implicature,and finds conversational implicature exists in both Chinese and English conversation.

Pragmatic Analysis on Conversational Implicature in Mansfield Park

Mansfield Park is one of Jane Austen's masterpieces. There are a lot of lively vivid dialogues in the novel. Conversational implicature is analyzed from the perspective of progmatics in Mansfield Park will be provided in this article.

On Grice's Conversational Implicature Theory

Conversational Implicature Theory is one important theory of pragmatics. It focuses on the implied meaning of the speakers and plays an important role in speech communication.

Views on Differences of Conversational Styles between Chinese and English

Cultural values determine and influence the communicative competence in a substantial way.Conversational styles are also demonstrated by cultural values which are obvious to be discovered in any cultures.The paper is aimed at making a brief analysis of differences between Chinese and English from the perspective of the intercultural communication for the promotion of language abilities for learners during the cultural exchanges.

Impacts of Conversational Implicature in English-Chinese Translation

Translation has always been acknowledged as one main branch of linguistics.Catford holds that translation is a specific linguistic theory.It’s necessary and important to study the relationship between translation and linguistic.Many scholars have proposed many opinions on the impacts of conversational implicature.From the academic perspective and on the basis of Grice,H.P’s conversational implicature theory,this article makes a careful analysis of conversational implicature and translation by giving exact definition and discussing the features of them.This article talks about the relationship between conversational implicature and translation by giving definitions and setting examples,hoping to help translation in further and make a great contribution to the development of translation.

Rate-distortion optimized frame dropping and scheduling for multi-user conversational and streaming video

We propose a Rate-Distortion (RD) optimized strategy for frame-dropping and scheduling of multi-user conversa- tional and streaming videos. We consider a scenario where conversational and streaming videos share the forwarding resources at a network node. Two buffers are setup on the node to temporarily store the packets for these two types of video applications. For streaming video, a big buffer is used as the associated delay constraint of the application is moderate and a very small buffer is used for conversational video to ensure that the forwarding delay of every packet is limited. A scheduler is located behind these two buffers that dynamically assigns transmission slots on the outgoing link to the two buffers. Rate-distortion side information is used to perform RD-optimized frame dropping in case of node overload. Sharing the data rate on the outgoing link between the con- versational and the streaming videos is done either based on the fullness of the two associated buffers or on the mean incoming rates of the respective videos. Simulation results showed that our proposed RD-optimized frame dropping and scheduling ap- proach provides significant improvements in performance over the popular priority-based random dropping (PRD) technique.

Conversational implicature in business negotiation

Conversational implicature is a feature of language in use. This paper is trying to explore the conversational implicature phenomenon in business negotiation. From pragmatics view, it analyses the potential meaning of break off the Negotiation and carry on the negotiation in business negotiation. Through carefully selected examples, it manages to show how to behave politely in business negotiation. It's hoped that this paper can help those who are in such field to achieve successful negotiation.

On the Role of Working Memory in the Comprehension of Conversational Implicatures

According to theory,working memory capacity should be related to comprehension ability,in this thesis,it tries to jus tify the relationship between working memory capacity and comprehension of conversational implicatures.Through theoretical hypothesizing and empirical demonstration,the relationship between working memory and the comprehension of conversational implicatures,especially role that the phonological loop plays in the comprehension of conversational implicatures caused by literal violation to the maxim of manner,is justified in the thesis.

The Application of Conversational Implicature in Doctor-Patient Conversation

The doctor-patient conversation is the major way of communication between doctors and patients.A good conversation helps to construct a harmonious doctor-patient relationship.This paper attempts to analyze the doctor-patient conversation by applying the Theory of Conversational Implicature.The theory is accepted as the cooperative principle consisting of four maxims:quantity,quality,relation,manner.This paper will analyse how the four maxims work and the violating of the maxims in the doctor-patient conversation.Through linguistic study of the conversation,we try to find out the problems in the doctor-patient conversation and to provide some directive linguistic suggestions to doctors and patients.

Analysis of Conversational Implicatures in Early Autumn

Early Autumn written by Langston Hughes is a short story about ex-lovers Mary and Bill who come across on Wash ington Square in a dusk of early autumn.The story is composed mainly by short conversation and scenery depiction.This paper investigates the characteristics of Mary and Bill through conversation analysis based on Cooperative Principle and Face Theory.

An Analysis on Irony from the Perspective of Conversational Implicature

Irony is a frequently occuring indirect act in daily interpersonal interaction.A number of novel ironic cases via deliberately flouting the four maxims of the Cooperative Principle are explained from the perspective of conversational implicature.This paper tend to explore the production of irony according to Grice’s Cooperative Principle and conversational implicature theory with some daily examples and the author hope that this paper is of certain help to further research concerning irony in the realm of pragmatics.

BERT for Conversational Question Answering Systems Using Semantic Similarity Estimation

Most of the questions from users lack the context needed to thoroughly understand the problemat hand,thus making the questions impossible to answer.Semantic Similarity Estimation is based on relating user’s questions to the context from previous Conversational Search Systems(CSS)to provide answers without requesting the user’s context.It imposes constraints on the time needed to produce an answer for the user.The proposed model enables the use of contextual data associated with previous Conversational Searches(CS).While receiving a question in a new conversational search,the model determines the question that refers tomore pastCS.Themodel then infers past contextual data related to the given question and predicts an answer based on the context inferred without engaging in multi-turn interactions or requesting additional data from the user for context.This model shows the ability to use the limited information in user queries for best context inferences based on Closed-Domain-based CS and Bidirectional Encoder Representations from Transformers for textual representations.

Deep Learning and Machine Learning-Based Model for Conversational Sentiment Classification

In the current era of the internet,people use online media for conversation,discussion,chatting,and other similar purposes.Analysis of such material where more than one person is involved has a spate challenge as compared to other text analysis tasks.There are several approaches to identify users’emotions fromthe conversational text for the English language,however regional or low resource languages have been neglected.The Urdu language is one of them and despite being used by millions of users across the globe,with the best of our knowledge there exists no work on dialogue analysis in the Urdu language.Therefore,in this paper,we have proposed a model which utilizes deep learning and machine learning approaches for the classification of users’emotions from the text.To accomplish this task,we have first created a dataset for the Urdu language with the help of existing English language datasets for dialogue analysis.After that,we have preprocessed the data and selected dialogues with common emotions.Once the dataset is prepared,we have used different deep learning and machine learning techniques for the classification of emotion.We have tuned the algorithms according to the Urdu language datasets.The experimental evaluation has shown encouraging results with 67%accuracy for the Urdu dialogue datasets,more than 10,000 dialogues are classified into five emotions i.e.,joy,fear,anger,sadness,and neutral.We believe that this is the first effort for emotion detection from the conversational text in the Urdu language domain.

Factor Analysis on Oral English Conversational Competence of College Students

Oral English is always a difficult part for Chinese students,some can speak several sentences while not clearly know others’saying;others can understand the speakers’words while not express their own meaning fluently.This paper will focus on what factors influences the college students’spoken English by the specific experiment and SPSS app,whose data shows four ones,namely Language Monitor,Meaning Negotiation,Turn Taking and Empathetic Exchange.Thus,the paper takes conclusions and poses some suggestions to oral English teaching according to the affecting causes.It is expecting to help improve the ability of the oral English Conversation for Chinese College students.

Cluster Analysis Based on Contextual Features Extraction for Conversational Corpus

Cluster analysis related to computational linguistics seldom concerned with Pragmatics level. Features of corpus on Pragmatics level related to specific situations, including backgrounds, titles and habits. To improve the accuracy of clustering for conversations collected from international students in Tsinghua University, it required contextual features. Here, we collected four-hundred conversations as a corpus and built it to Vector Space Model. With the Oxford-Duden Dictionary and other methods we modified the model and concluded into three groups. We testified our hypothesis through self-organizing map neural network. The result suggested that the modified model had a better outcome.

A Study of Teachers' Conversational Fillers in Transitional Relevance Point

Based on the theory of conversation, turn,and turn-taking, this paper selects three non-English major college English teachers' English lessons as the research subjects. Through classroom observation, recording, video and transcript, and by using Microsoft Excel2007 to collect and analyze data, this paper focuses on the teachers' conversational fillers in transitional relevance point. This study will contribute significantly to teachers' taking measures to reduce the impacts of negative transfer of mother tongue on natural and fluent communication and improve the quality of communicative language teaching.