Papers with human agents

5 papers
The economic trade-offs of large language models: A case study (2023.acl-industry)

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Challenge: Large Language Models (LLMs) are a natural fit for contact-based customer service, but their efficacy must be balanced with the cost of training and serving them.
Approach: They propose a cost framework for evaluating an NLP model’s utility for the enterprise as a function of the usefulness of the responses that they generate.
Outcome: The proposed model can be used to help human agents handle complex customer service calls and can be modified to improve their performance.
LLM-Based Dialogue Labeling for Multiturn Adaptive RAG (2025.emnlp-industry)

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Challenge: Retrieval-Augmented Generation (RAG) models integrate large language models with external knowledge retrieval . however, building multi-turn RAG-based chatbots for real-world customer service requires additional complexities.
Approach: They propose methods to automatically generate labels for adaptive retrieval components using real customer-agent dialogue data.
Outcome: The proposed method generates labels for components using real customer-agent dialogue data.
Exploring a Unified Sequence-To-Sequence Transformer for Medical Product Safety Monitoring in Social Media (2021.findings-emnlp)

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Challenge: Adverse Events (AEs) are harmful events resulting from the use of medical products.
Approach: They propose a model that combines sequence-to-sequence learning with language transfer capabilities to improve model robustness.
Outcome: The proposed approach achieves strong performance over baselines on English benchmarks.
When and Who? Conversation Transition Based on Bot-Agent Symbiosis Learning Network (2020.coling-main)

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Challenge: a bot-agent symbiosis is a method for transparent conversation transition in online customer service applications.
Approach: They propose a bot-agent symbiosis approach to solve conversation transition problems . they provide user feedback and develop deep neural networks to predict the NPS .
Outcome: The proposed approach outperforms state-of-the-art methods on real-time data generated from an online service support platform.
Language Models as Agent Models (2022.findings-emnlp)

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Challenge: Language models (LMs) are trained on collections of documents written by individual human agents to achieve specific goals in the outside world.
Approach: a new study shows that language models are models of communicative intentions in a specific, narrow sense . despite recent progress, today's language models still make odd predictions and conspicuous errors .
Outcome: a survey of LMs shows that they can model communicative intentions in a specific, narrow sense . despite recent progress, current models still make odd predictions and conspicuous errors .

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