TL;DR:
- deepset introduces Groundedness Observability, a revolutionary capability on its cloud platform.
- This addresses the challenge of hallucinations in LLM-based GenAI responses.
- Groundedness Observability provides quantifiable scores for response accuracy and factuality.
- It empowers developers to fine-tune RAG systems and identify optimal retrieval methods.
- Source Reference Prediction enhances response quality with academic-style citations.
- deepset prioritizes data privacy and offers private cloud deployment options.
Main AI News:
In the ever-evolving landscape of Natural Language Processing (NLP), deepset has emerged as a leader with its Haystack open-source framework for building NLP services. Now, deepset is taking a quantum leap in the field by introducing a revolutionary capability to its cloud platform that addresses one of the most pressing challenges faced by Large Language Models (LLM) – hallucinations. This breakthrough technology promises to provide invaluable insights into the precision and accuracy of LLM generative AI (GenAI) responses.
Hallucinations have long been a formidable obstacle when it comes to the widespread adoption of LLM models within enterprises. While techniques like RAG systems have been employed to mitigate these issues, LLMs still tend to generate responses that either place data in the wrong context or fabricate entirely fictitious information. According to Mathis Lucka, Head of Product at deepset, “From GPT-4 to the smaller open-source models, hallucinations remain a challenge, even with RAG.”
To combat this challenge head-on, deepset has introduced the Groundedness Observability feature, which serves as a game-changer for enterprises seeking reliable GenAI applications. This innovative capability measures how well the answers generated by LLMs are grounded in the specific data provided by the user. It offers a quantifiable score that reveals the accuracy and factuality of an LLM’s output, including metrics on tone, specific document sources, and frequency of source usage.
The Groundedness Observability Dashboard not only empowers developers to fine-tune their RAG systems, models, and prompts for more dependable responses but also aids in identifying optimal hyperparameters for retrieval processes. This ensures that organizations can select the most suitable LLM for their unique needs, depending on the type of data and use cases. Moreover, it helps optimize the volume of data fed into LLMs, reducing overall costs in the process.
As Milos Rusic, Co-founder and CEO of deepset, highlights, “Picking the right LLM for your use case is a significant challenge, and different LLMs may have varying strengths and weaknesses. This is something you can address with Groundedness Observability.”
It’s important to note that deepset’s Groundedness Observability Dashboard is LLM-agnostic, offering users the flexibility to assess the accuracy and fidelity of any LLM and vendor of their choice.
In addition to this groundbreaking capability, deepset is introducing Source Reference Prediction to its cloud platform. This feature elevates confidence in LLM response quality by adding academic-style citations to each generated answer. These references trace back to the original document sources, providing users with the means to independently verify the accuracy of the information.
Deepset places a strong emphasis on data privacy, adhering to SOC 2 Type II requirements to safeguard customer data. For those enterprises seeking an extra layer of security, the option to run deepset within a private cloud environment is also available.
With both Groundedness Observability and Source Reference Prediction, deepset is reaffirming its commitment to building a robust trust layer within GenAI applications. As Mathis Lucka succinctly puts it, “Readers should be most excited about reliably creating applications that are trustworthy.” The availability of these tools is poised to revolutionize the world of large language models, paving the way for a new era of dependable and trustworthy applications.
Conclusion:
The introduction of deepset’s Groundedness Observability and Source Reference Prediction represents a significant leap forward in the GenAI market. These tools provide a robust solution to the long-standing challenge of hallucinations in LLM responses, offering businesses a means to enhance the accuracy and reliability of their AI applications. With a focus on data privacy and flexibility, deepset is poised to drive the adoption of GenAI across various industries, ensuring that applications built on large language models can be trusted and relied upon.
