Types of undesirable outputs you can get from LLMs

This article explores the risks of false, harmful outputs from large language models and provides actionable insights for business leaders to mitigate them.
In: LLMs, Business

LLMs like GPT-4 and PaLM have exploded in popularity recently, showcasing an impressive ability to generate human-like text, translate languages, and automate content creation. However, these powerful AI systems also carry risks of generating harmful, biased, or nonsensical outputs.


  • Hallucinations and Fabrications
  • Data Poisoning Risks
  • Toxic Language Generation
  • Toxic Language Generation
  • Unstable Task Performance
  • Lack of Verification
  • Mitigating Harmful Outputs

Let's dive into it 🤿

As business leaders look to leverage LLMs, it's crucial to understand the types of undesirable outputs that can occur and set proper safeguards. This post will provide an overview of the key issues, some technical details business users should know, and mitigation techniques.

Hallucinations and Fabrications

A primary concern is that LLMs will "hallucinate" - make up plausible but totally incorrect or illogical content. For example, if prompted to summarize key points from a meeting, the LLM could fabricate events or decisions that never occurred!

This happens because the models develop statistical relationships between words through training, rather than a factual understanding of the world. They can convincingly generate fake names, quotes, and other bogus details that feel true.

Data Poisoning Risks

A related danger is training the models on misinformation or problematic data sources, whether intentionally or by accident. This "data poisoning" causes models to inherit the biases or falsehoods within that data.

For example, scraping websites with conspiracy theories or offensive content could teach the models to generate similarly harmful text. Thorough data curation is essential but challenging at the massive scale of LLMs.

Toxic Language Generation

Due to absorbing patterns in their training data, LLMs often naturally learn to output harmful language like hate speech, abuse, or profanity when prompted in certain ways.

Although curating training data helps, models still reflect the long tail of the internet's most toxic content. Ongoing research aims to develop "antidotes" by re-training LLMs to avoid producing harmful outputs.

Unstable Task Performance

 LLMs can exhibit wild inconsistencies, like perfectly summarizing text and then spewing nonsense given a very similar prompt. Slight input tweaks lead to hugely varying outputs.

This likely stems from the pseudo-randomness involved in decoding the models' probabilistic outputs. Reducing this variance remains an open challenge.

Lack of Verification

A key limitation of LLMs is they have no ability to verify the accuracy or factual correctness of the content they generate. They can fabricate convincing outputs with high confidence regardless of the truth.

This is an inherent blind spot not easily addressed without a retrieval mechanism to reference external knowledge. For business use cases, integrating human verification is advised before acting on any high-stakes LLM outputs.

Mitigating Harmful Outputs

While LLMs carry inherent risks, several strategies can help reduce generating incorrect or toxic content:

  • Carefully engineered prompts - Well-designed prompts steer models towards beneficial outputs and avoid triggers for hallucination or toxicity. This "prompt programming" is an art that requires insight and iteration.
  • Output filtering - Tools can automatically filter out known harmful language, misinformation, or inappropriate content generated. This provides an additional safety net beyond careful prompting.
  • Accuracy feedback - Allowing users to flag incorrect outputs and feeding that signal back into the model for incremental improvements can enhance truthfulness over time.
  • Retrieval augmentation - Connecting LLMs to external knowledge sources like Wikipedia to verify facts acts as a safety mechanism. Models are kept honest by grounding them with outside references.
  • Ongoing model training - Continued training focused on truthfulness and safety, such as Anthropic's Constitutional AI approach, can instill beneficial behaviors in models.
  • Human-in-the-loop - For high-stakes applications, having a human reviewer validate an LLM's outputs before acting provides an essential layer of protection and oversight.

With the right combination of prompt engineering, software guardrails, human oversight, and ongoing training, businesses can feel more confident deploying LLMs for useful purposes while keeping risks contained.

The Promise and the Peril

Large language models represent an enormous advance in AI capabilities. However, their propensity for fabricating plausible-sounding but false or toxic content poses real risks if deployed carelessly.

Applying guardrails like input safety checks, output filtering, and human oversight is essential to benefit from their utility while minimizing harm. As with any powerful technology, LLMs must be handled thoughtfully to unlock their full potential.

The key is finding the right balance - appreciating their limitations and intelligently mitigating risks while also enabling helpful applications that move businesses and society forward. Responsible use unlocks the promise while avoiding the pitfalls.

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Armand 😎

Written by
Armand Ruiz
I'm a Director of Data Science at IBM and the founder of NoCode.ai. I love to play tennis, cook, and hike!
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