# Bringing the State-of-the-Art to Customers: A Neural Agent Assistant Framework for Customer Service Support

arXiv: https://arxiv.org/abs/2302.03222 · HTML: https://mlsanity.com/paper/2302.03222/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 23 references that are in the map.
- First posted on arXiv 7 Feb 2023.

## Builds on (most cited first, 17 of 17)

- [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)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT](https://mlsanity.com/paper/2004.12832/) (2020)
- [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://mlsanity.com/paper/1911.00536/) (2019)
- [Improving Neural Machine Translation Models with Monolingual Data](https://mlsanity.com/paper/1511.06709/) (2015)
- [Passage Re-ranking with BERT](https://mlsanity.com/paper/1901.04085/) (2019)
- [ELI5: Long Form Question Answering](https://mlsanity.com/paper/1907.09190/) (2019)
- [Efficient Intent Detection with Dual Sentence Encoders](https://mlsanity.com/paper/2003.04807/) (2020)
- [An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction](https://mlsanity.com/paper/1909.02027/) (2019)
- [CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review](https://mlsanity.com/paper/2103.06268/) (2021)
- [Bias Out-of-the-Box: An Empirical Analysis of Intersectional Occupational Biases in Popular Generative Language Models](https://mlsanity.com/paper/2102.04130/) (2021)
- [COIL: Revisit Exact Lexical Match in Information Retrieval with Contextualized Inverted List](https://mlsanity.com/paper/2104.07186/) (2021)
- [Effectiveness of Pre-training for Few-shot Intent Classification](https://mlsanity.com/paper/2109.05782/) (2021)
- [Multimodal Conversational AI: A Survey of Datasets and Approaches](https://mlsanity.com/paper/2205.06907/) (2022)

## Built on by (most cited first, 6 of 6)

- [AgentMisalignment: Measuring the Propensity for Misaligned Behaviour in LLM-Based Agents](https://mlsanity.com/paper/2506.04018/) (2025)
- [FAIIR: Building Toward A Conversational AI Agent Assistant for Youth Mental Health Service Provision](https://mlsanity.com/paper/2405.18553/) (2024)
- [Building Customer Support AI Agents at 100M-User Scale: An Evaluation-Driven Framework](https://mlsanity.com/paper/2606.08867/) (2026)
- [Unveiling Dual Quality in Product Reviews: An NLP-Based Approach](https://mlsanity.com/paper/2505.19254/) (2025)
- [textitGrahak-Nyay: Consumer Grievance Redressal through Large Language Models](https://mlsanity.com/paper/2507.04854/) (2025)
- [Learning Selective LLM Autonomy from Copilot Feedback in Enterprise Customer Support Workflows](https://mlsanity.com/paper/2604.23855/) (2026)

## Abstract (arXiv)

Building Agent Assistants that can help improve customer service support requires inputs from industry users and their customers, as well as knowledge about state-of-the-art Natural Language Processing (NLP) technology. We combine expertise from academia and industry to bridge the gap and build task/domain-specific Neural Agent Assistants (NAA) with three high-level components for: (1) Intent Identification, (2) Context Retrieval, and (3) Response Generation. In this paper, we outline the pipeline of the NAA's core system and also present three case studies in which three industry partners successfully adapt the framework to find solutions to their unique challenges. Our findings suggest that a collaborative process is instrumental in spurring the development of emerging NLP models for Conversational AI tasks in industry. The full reference implementation code and results are available at urlhttps://github.com/VectorInstitute/NAA