As we continue to learn how harness the power of Large Language Models (LLMs), we must also grapple with their limitations. One such limitation is the phenomenon of “hallucinations.”. That’s where LLMs generate text that is erroneous, nonsensical, or detached from reality. In today’s brief update I’m going to share 5 powerful techniques for mitigating LLM hallucinations, and…
As usual, at the end of this post, I’ll provide you a special event to a free live online training event where you can go for hands-on training for how to tackle the hallucinations problem in real life.
The problem with LLM hallucinations
The first problem with LLM hallucinations is, of course, that they’re annoying. I mean, it would be ideal if users didn’t have to go through all model outputs with a finetooth comb every time they want to use something the create with AI.
But the problems with LLM hallucinations are more grave.
LLM hallucinations can result in the following grievances:
- The spread of misinformation
- The exposure of confidential information, and
- The fabrication of unrealistic expectations about what LLMs can actually do.
That said, there are effective strategies to mitigate these hallucinations and enhance the accuracy of LLM-generated responses. And without further ado, here are 5 powerful techniques for mitigating LLM hallucinations.
5 powerful techniques for detecting & mitigating LLM hallucinations
The techniques for detecting and mitigating LLM hallucinations may be simpler than you think…
These are the most popular methodologies right now…
1. Log probability
The first technique involves using log probability. Research shows that token probabilities are a good indicator of hallucinations. When LLMs are uncertain about their generation, it shows up. Probability actually performs better than entropy of top-5 tokens in detecting hallucinations. Woohoo!
2. Sentence similarity
The second technique for mitigating LLM hallucinations is sentence similarity. This method involves comparing the generated text with the input prompt or other relevant data. If the generated text deviates significantly from the input or relevant data, it could be a sign of a hallucination. (check yourself before you wreck yourself? 🤪)
3. SelfCheckGPT
SelfCheckGPT is a third technique that can be used to mitigate hallucinations. This method involves using another LLM to check the output of the first LLM. If the second LLM detects inconsistencies or errors in the output of the first LLM, then that could be a sign of a hallucination.
4. GPT-4 prompting
GPT-4 prompting is a powerful technique for mitigating hallucinations in LLMs.
Here are the top three techniques for using GPT-4 prompting to mitigate LLM hallucinations:
- Provide precise and detailed prompts – This involves crafting precise and detailed prompts that deliver clear, specific, and detailed guidance to help the LLM generate more accurate and reliable text. This technique reduces the chances of the LLM filling in gaps with invented information, thus mitigating hallucinations.
- Provide contextual prompts – Using contextual prompts involves providing the LLM with relevant context through the prompt. The context can be related to the topic, the desired format of the response, or any other relevant information that can guide the LLM’s generation process. By providing the right context, you can guide the LLM to generate text that is more aligned with the desired output, thus reducing the likelihood of hallucinations.
- Augment your prompts – Prompt augmentation involves modifying or augmenting your prompt to guide the LLM towards a more accurate response. For instance, if the LLM generates a hallucinated response to a prompt, you can modify the prompt to make it more specific or to guide the LLM away from the hallucinated content. This technique can be particularly effective when used in conjunction with a feedback loop, where the LLM’s responses are evaluated, and the prompts are adjusted based on the evaluation
These techniques can be highly effective in mitigating hallucinations in LLMs, but be careful they’re certainly not foolproof!
5. G-EVAL
The fifth technique is G-EVAL. This is a tool that can be used to evaluate the output of an LLM. It can detect hallucinations by comparing the output of the LLM with a set of predefined criteria or benchmarks.
Interested in learning more about how to efficiently optimize LLM applications?
If you’re ready for a deeper look into what you can do to overcome the LLM hallucination problem, then you’re going to love the free live training that’s coming up on Nov 8 at 10 am PT.
Topic: Scoring LLM Results with UpTrain and SingleStoreDB
In this 1-hour live demo and code-sharing session, you’ll get robust best practices for integrating UpTrain and SingleStoreDB to achieve real-time evaluation and optimization of LLM apps.
Join us for a state-of-the-art showcasing of the powerful and little-known synergy between UpTrain’s open-source LLM evaluation tool and SingleStoreDB’s real-time data infrastructure!
Within this session, you’ll get the chance to witness how effortlessly you can score, analyze, and optimize LLM applications, allowing you to turn raw data into actionable insights in real-time.
You’ll also learn just how top-tier companies are already harnessing the power of UpTrain to evaluate over 8 million LLM responses. 🤯
Sign up for our free training today and unlock the power of real-time LLM evaluation and optimization.
Pro-tip: If you like this type of training, consider checking out other free AI app development trainings we are offering here, here, here, here, here, here, and here.
Hope to see you there!
Cheers,
Lillian
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