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Evaluating Quality of Chatbots and Intelligent Conversational Agents

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arxiv 1704.04579 v1 pith:EU65QPDX submitted 2017-04-15 cs.CY cs.SE

Nicole M. Radziwill , Morgan C. Benton This is my paper · ORCID
classification cs.CYcs.SE
keywords chatbotsqualityconversationaldevelopmentagentsassessmentattributeschatbot
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Chatbots are one class of intelligent, conversational software agents activated by natural language input (which can be in the form of text, voice, or both). They provide conversational output in response, and if commanded, can sometimes also execute tasks. Although chatbot technologies have existed since the 1960s and have influenced user interface development in games since the early 1980s, chatbots are now easier to train and implement. This is due to plentiful open source code, widely available development platforms, and implementation options via Software as a Service (SaaS). In addition to enhancing customer experiences and supporting learning, chatbots can also be used to engineer social harm - that is, to spread rumors and misinformation, or attack people for posting their thoughts and opinions online. This paper presents a literature review of quality issues and attributes as they relate to the contemporary issue of chatbot development and implementation. Finally, quality assessment approaches are reviewed, and a quality assessment method based on these attributes and the Analytic Hierarchy Process (AHP) is proposed and examined.

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Cited by 2 Pith papers

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  1. Consistency of Compositional Generalization across Multiple Levels

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    Large vision-language models score below 50 percent on a new cross-level compositional consistency benchmark, and a progressive meta-optimization training framework improves consistency on several VQA and TVG baselines.

  2. ReSURE: Regularizing Supervision Unreliability for Multi-turn Dialogue Fine-tuning

    cs.CL 2025-08 conditional novelty 4.0 of 10

    ReSURE reduces the harm of noisy dialogue data during fine-tuning by grouping samples by dialogue depth and softly down-weighting high-loss examples, improving multi-turn benchmarks modestly.

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