A set of formatted clinical documents as dictated by the physicians to train medical AI models.

Bitext - Retail Banking Tagged Training Dataset for LLM-based Virtual Assistants

  Overview

This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [Retail Banking] sector can be easily achieved using our two-step approach to LLM… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-retail-banking-llm-chatbot-training-dataset.

Problem Statement

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A leading e-commerce platform faced challenges in providing timely and efficient customer support. The volume of inquiries often overwhelmed support teams, resulting in delayed responses, increased operational costs, and dissatisfied customers. The company needed a solution to automate responses to common customer queries while maintaining personalized service

Challenge

Implementing an automated customer support solution came with specific hurdles:

Handling diverse inquiries, including product details, order status, and returns, with high accuracy.

Integrating the chatbot with existing systems like CRM and order management.

Ensuring a seamless customer experience while maintaining conversational quality and natural interactions.

Solution Provided

An AI-driven demand forecasting system was developed, utilizing time series forecasting models and advanced analytics platforms to predict product demand accurately. The solution was designed to:

Address common customer inquiries with pre-trained conversational models.

Redirect complex queries to human agents seamlessly.

Operate 24/7 across multiple communication channels, including the website, mobile app, and social media.

Development Steps

Data Collection

Compiled historical customer inquiries and support responses to build a robust dataset for training the chatbot.

Preprocessing

Cleaned and categorized data to create intent libraries and FAQs for training NLP models.

Model Training

Trained the chatbot using NLP algorithms to recognize intents and entities, ensuring accurate responses. Enhanced the model with machine learning to adapt to customer-specific language and trends.

Integration

Integrated the chatbot with CRM, order management, and support ticketing systems to provide real-time information on orders and account details.

Deployment

Rolled out the chatbot on the e-commerce platform’s website, app, and social media channels, enabling round-the-clock support.

Continuous Improvement

Established a feedback loop to monitor chatbot performance, user satisfaction, and areas for improvement, refining the system continuously.

Results

24/7 Customer Support Availability

The chatbot provided uninterrupted customer support, addressing inquiries outside regular business hours.

Reduced Response Times

Automated responses decreased average response times by 50%, ensuring prompt assistance for customers.

Lowered Operational Costs

The chatbot reduced dependency on human agents for routine inquiries, cutting support costs significantly.

Improved Customer Satisfaction

Timely and accurate responses enhanced the customer experience, leading to positive feedback and increased brand loyalty.

Scalable Solution

The chatbot system scaled effortlessly to handle growing customer volumes during peak periods, such as holiday sales.

LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset

This dataset contains one million real-world conversations with 25 state-of-the-art LLMs. It is collected from 210K unique IP addresses in the wild on the Vicuna demo and Chatbot Arena website from April to August 2023. Each sample includes a conversation ID, model name, conversation text in OpenAI API JSON format, detected language tag, and OpenAI moderation API tag. User consent is obtained through the "Terms of… See the full description on the dataset page: https://huggingface.co/datasets/lmsys/lmsys-chat-1m.

Dataset Card for Alpaca

  Dataset Summary

Alpaca is a dataset of 52,000 instructions and demonstrations generated by OpenAI's text-davinci-003 engine. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better. The authors built on the data generation pipeline from Self-Instruct framework and made the following modifications:

The text-davinci-003 engine to generate the instruction data instead… See the full description on the dataset page: https://huggingface.co/datasets/tatsu-lab/alpaca.

This dataset consists of 79 dialogs between a human user and a chatbot in English language. This data was collected during an online experiment conducted by the research group "Information Systems & Service Design" at the Karlsruhe Institute of Technology (KIT). Experimental task: Participants were asked to interact with a chatbot to find out whether they could save money by switching to a better mobile phone plan. Additionally, there were shown a fictitious copy of last month's mobile phone bill. During the conversation, the chatbot asked about the participant's usage patterns (e.g., how much data was used) and recommended a randomly generated plan that better met the participant’s requirements. For more information, see Gnewuch et al. (2018). If you have any questions, please contact us via email (info@chatbotresearch.com) or visit https://chatbotresearch.com. WARNING! Some dialogs contain profanity and/or offensive language. Profanity was not removed because it is important for calculating sentiment scores. PUBLICATIONS / REFERENCES Gnewuch, U., Morana, S., Adam, M. T. P., and Maedche, A. 2018. “Faster Is Not Always Better: Understanding the Effect of Dynamic Response Delays in Human-Chatbot Interaction,” in Proceedings of the 26th European Conference on Information Systems (ECIS 2018), Portsmouth, United Kingdom. Feine, J., Morana, S., and Gnewuch, U. 2019. “Measuring Service Encounter Satisfaction with Customer Service Chatbots using Sentiment Analysis,” in Proceedings of the 14th International Conference on Wirtschaftsinformatik (WI 2019), Siegen, Germany, February 24–27.