Latest Updates in Bing Search Engine You Should Know

Table of content

Microsoft has recently made a game-changing update to the Bing search engine. This update claims faster results at a reduced cost without compromising accuracy. Bing takes an unexpected step by combining Large Language Models (LLMs) and Small Language Models (SLMs) to enhance its search results. On the other hand, integrating NVIDIA’s TensorRT-LLM technology reduced operational costs and lowered the 95th percentile latency to 4.76 seconds per batch.

“At Bing, we are always pushing the boundaries of search technology,” Microsoft states. Over the years, traditional transformer models have struggled to keep up with the growing complexity of search queries. The increasing demand for more powerful and efficient search capabilities led to the integration of LLMs and SLMs with the search engine.

What Were the Key Challenges in Bing Search?

1. Performance vs. Cost:

2. Balancing Quality and Speed:

3. Latency and Throughput:

What are the Latest Bing Search Updates?

1. Combines LLMs and SLMs to Enhance Search

Integrating LLMs and SLMs allows Bing to balance power and efficiency. Large Language Models, known for their ability to process intricate search queries, are complemented by SLMs, which provide significantly faster throughput.

According to Microsoft, SLMs offer a 100x throughput improvement over LLMs, allowing Bing to process and understand search queries precisely. Bing ensures that searching is easier and more user-friendly by providing relevant search results and integrating complex and LLM interfaces.

What are Large Language Models (LLMs)?

As a part of the vast family of advanced AI, Large Language Models are built to deal with complex language data effortlessly. In Bing, LLMs help comprehend and process complex search strings where meanings are multifarious, deeper, and contextualized.

What are Small Language Models (SLMs)?

Small Language Models (SLMs) are simplified and lightweight models. In Bing, SLMs are much more efficient than LLMs, achieving throughput speeds that LLMs beat up by 100 times. SLMs work well with technologies like NVIDIA TensorRT-LLM, enhancing their performance by reducing latency and operational costs.

2. NVIDIA technology to Reduce Operational Costs and Improved Latency

Microsoft has addressed these challenges by training SLMs, which process queries with a throughput improvement of nearly 100 times over LLMs. In addition, integrating NVIDIA TensorRT-LLM technology has been pivotal in optimizing performance and cost efficiency.

How Does It Work?

TensorRT-LLM employs the Smooth Quant technique, enabling INT8 precision for activations and weights without compromising accuracy. This method improves model performance and makes search inference more efficient.

This optimization has led to remarkable improvements:

36% reduction in latency: Bing’s latency is dropping from 4.76 seconds per batch to 3.03 seconds per batch (20 queries).
57% increase in throughput: The system can now handle 6.6 queries per second per instance, up from 4.2 queries per second.
Reduced operational costs: TensorRT-LLM minimizes the time and resources needed to run large models, allowing Bing to reinvest in future innovations.

These advancements make Bing one of the most efficient search engines, capable of handling growing complexities in search queries.

Impact of the Update?

Bing’s Deep Search feature leverages real-time data and SLMs to deliver highly relevant results. The integration of TensorRT-LLM has further refined this capability, enabling faster and more reliable performance.

Microsoft’s technical report reveals that TensorRT-LLM significantly enhances the Deep Search pipeline, ensuring users get the most relevant results instantly. This is particularly beneficial for complex queries where context and precision are paramount.

Why Do These Updates Matter?

Bing’s move to integrate LLMs, SLMs, and TensorRT optimization may hit the traffic of Google, Yahoo, and other search engines. Regarding the complex requirements of the modern search market, this enhancement places Bing as a progressive search engine. Bing sets the benchmark for efficiency and quality in search technology by adopting smaller, faster language models and advanced optimization techniques.

Benefits of New Bing Search Updates for Users

The recent updates bring a host of advantages for users:

1. Faster Response Times: Bing’s optimized inference system processes search queries more quickly, reducing wait times and enhancing the overall search experience.

2. Improved Accuracy: The advanced capabilities of SLM ensure more contextualized and relevant search results, even for complex or multi-layered queries.

3. Cost Efficiency: By reducing operational costs, Bing can channel resources into further innovations, benefiting users with continuous improvements.

Looking Ahead

Microsoft’s update focuses on improving Bing so that it can compete actively amongst other search engine solutions. Such upgrades benefit the end user’s experience and set the stage for the future of search where speed and accuracy cannot be compromised.

If there is a sense that the full effect of these changes will take time to materialize, it is evident that the latest updates made to Bing are worthwhile. These updates in Bing will likely provide better and more efficient search results in the future.

Will the Bing search engine surpass competitors like Google, Yahoo, and Yandex? This query is yet to be uncovered. Our team will continue to push its limits to bring the latest tech updates. Stay connected with Nodespace Innventive Lab!

FAQ'S

What is cloud computing?

Cloud computing provides easy, on-demand access to online resources. It offers storage, databases, and processing power through cloud providers. This eliminates the need for physical servers and maintenance.

What is the evolution of cloud computing?

The evolution of cloud computing is as follows –

1950 – Introduces Mainframe Timesharing by John McCarthy
1969 – ARPANET by J.C.R Licklinder
1970 – Virtualistation software launched
1997 – Cloud Computing by Prof. Ramnath Chellappa
1999- Salesforce
2006- Amazon Launches Elastic Compute cloud (EC2)
Simple storage service (S3)
2020- $266 billion estimated global public cloud service market