# Comprehensive Framework for Evaluating Conversational AI Chatbots

arXiv: https://arxiv.org/abs/2502.06105 · HTML: https://mlsanity.com/paper/2502.06105/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 10 references that are in the map.
- First posted on arXiv 10 Feb 2025.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions](https://mlsanity.com/paper/2410.12837/) (2024)
- [A Comprehensive Survey of Bias in LLMs: Current Landscape and Future Directions](https://mlsanity.com/paper/2409.16430/) (2024)
- [Comprehensive Study on Sentiment Analysis: From Rule-based to modern LLM based system](https://mlsanity.com/paper/2409.09989/) (2024)
- [Evaluation of LLMs Biases Towards Elite Universities: A Persona-Based Exploration](https://mlsanity.com/paper/2407.12801/) (2024)

## Built on by (most cited first, 2 of 2)

- [Counterfactual Fairness Evaluation of LLM-Based Contact Center Agent Quality Assurance System](https://mlsanity.com/paper/2602.14970/) (2026)
- [FinLifeBench: Exhaustive Life-Event History and Financial-State Reconstruction from Longitudinal Banking Dialogue](https://mlsanity.com/paper/2609.01198/) (2026)

## Abstract (arXiv)

Conversational AI chatbots are transforming industries by streamlining customer service, automating transactions, and enhancing user engagement. However, evaluating these systems remains a challenge, particularly in financial services, where compliance, user trust, and operational efficiency are critical. This paper introduces a novel evaluation framework that systematically assesses chatbots across four dimensions: cognitive and conversational intelligence, user experience, operational efficiency, and ethical and regulatory compliance. By integrating advanced AI methodologies with financial regulations, the framework bridges theoretical foundations and real-world deployment challenges. Additionally, we outline future research directions, emphasizing improvements in conversational coherence, real-time adaptability, and fairness.