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Community Blog Lobechat Uses the WolframAlpha MCP Tool to Reduce LLM Hallucinations

Lobechat Uses the WolframAlpha MCP Tool to Reduce LLM Hallucinations

This article introduces how Lobechat utilizes the WolframAlpha MCP Tool to reduce hallucinations in LLMs by leveraging precise computational capabilities.

By Zi You

Why We Need WolframAlpha

With the rapid development of artificial intelligence technology, large language models (LLMs) such as GPT-4 have achieved significant accomplishments in natural language processing and generation. However, these models often exhibit a phenomenon known as "hallucination" when dealing with complex scientific and mathematical problems, generating incorrect or unreasonable results. This phenomenon not only affects the reliability of the models but also limits their wide application in fields such as education and research. [1]

When generating text, large language models often rely on patterns and associations from their training data, lacking a deep understanding of the physical laws and logical rules of the real world. This leads to the generation of seemingly reasonable but actually incorrect answers when dealing with scientific and mathematical problems.

For example, when solving a physics problem, the model might ignore unit conversions, the application of physical laws, or even make basic errors in mathematical calculations. This hallucination phenomenon not only affects the correctness of problem-solving but also makes the models seem overwhelmed when faced with complex issues.

Wolfram Alpha is a powerful computational knowledge engine capable of addressing a variety of scientific, mathematical, and technical problems. It is based on a comprehensive knowledge base of mathematics and science, enabling precise calculations, symbolic operations, and formula derivations. Compared to large language models, Wolfram Alpha has a significant advantage in handling complex mathematical formulas, physical laws, and scientific concepts. It can perform calculations accurately, avoiding errors that may arise from the limitations of the models themselves. [2]

The Higress MCP market has recently launched the WolframAlpha LLM API, which can be accessed via the Wolfram MCP format. It supports various calling forms, including Lobechat, Cline, Cherry Studio, and DeepChat. Currently, there is a free trial limit of 10 calls per month for individual users, and everyone is welcome to try it out!

Configuration Process

Accessing the WolframAlpha MCP Tool

Enter the Higress MCP market homepage: https://mcp.higress.ai/ , click WolframAlpha

Click on WolframAlpha

1

In WolframAlpha, use a custom or trial API-KEY to generate the URL.

2

Select and save the generated Streamable HTTP URL for subsequent configuration.

3

Configure Higress MCP in Lobechat

Install Lobechat

Lobechat offers both an online version and an open-source version, and the latest versions support the use of MCP tools. The online version can be accessed at https://lobechat.com/ , and Lobechat officially provides each new user with 50,000 free credits for trials. The online paid version supports added services such as file & knowledge base, more model services, and cloud services.

4

For the open-source version, users need to have a Docker environment locally and execute the following command for one-click installation:

docker run -d -p 3210:3210 \
       --name lobe-chat \
       lobehub/lobe-chat:1.82.4

In the settings interface, you can configure the API-KEY corresponding to different model providers.

5

Configure Higress MCP

In the plugin store of Lobechat, select Custom Plugin:

6

Select the MCP plugin, specify the Streamable HTTP mode, fill in the URL obtained in the previous step, and then install the plugin.

7

In the dialog interface, enable the wolframAlpha plugin.

8

Test Cases

For some basic reasoning and common knowledge, integrating WolframAlpha can effectively resolve the hallucination issues during the reasoning process. Additionally, for basic mathematical functions such as calculations and plotting, WolframAlpha can also perform well.

Mathematical Reasoning

For reasoning difficulties in very unconventional mathematical knowledge, such as the following question:

Is 2^136279841-1 a prime number?

9

When WolframAlpha is not called, the complexity of the problem may prevent the model from providing an accurate answer.

10

Daily Calculations

For some everyday math problems, WolframAlpha can also assist in the calculations:

If I had $10,000 worth of gold in 1st January 2024, how much would it be worth in 1st January 2025?

11

Image Plotting

Additionally, WolframAlpha also supports some basic image plotting and generation functionalities:

Get the distribution of prime numbers within 10,000 and use appropriate plotting methods to show the change in the number of prime numbers per thousand

12

In addition, if the WolframAlpha LLM API encounters an error during the call, it will suggest a better way to ask based on the returned results. The Agent will also optimize the question format after receiving similar command responses to call the tool again and obtain correct results.

Outlook

By integrating the Wolfram Alpha tool, we can effectively address the hallucination phenomena encountered by large language models when handling scientific and mathematical problems. The precise computational capabilities and extensive knowledge base of WolframAlpha can compensate for the shortcomings of large language models, enhancing their accuracy and reliability in solving complex problems. In the future, as technology continues to advance, this integrated approach is expected to find applications in more fields, driving broader developments in artificial intelligence technology.

The Higress MCP market currently features over 40 MCP services, including search, sandbox tools, basic information queries, and other services. We welcome you to connect and use them!

[1] https://arxiv.org/html/2308.05713v4
[2] https://products.wolframalpha.com/api

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13

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