These days, it feels like Large Language Models (LLMs) and conversational agents are taking over the internet. We're asking them questions and expecting accurate, truthful answers. But is that expectation realistic? Recent research suggests that trusting LLMs implicitly can be risky. As someone who's been working in data science and AI for nearly a decade, specializing in NLP and agentic AI, I've been diving into the latest findings and want to share some key concerns.
Check out my full video diving into the research papers: https://www.youtube.com/watch?v=NUlNuPuHudY
The Lie Detector: LLMs Can Intentionally Deceive
One of the most unsettling discoveries is that LLMs aren't just prone to occasional "hallucinations" (unintentional errors); they can actually lie. A study by researchers at Carnegie Mellon University ("Can LLMs Lie? Investigations Beyond Hallucination") demonstrates that LLMs can discern between truth and falsehood within their internal systems. They can be aware that something is a lie and still choose to provide incorrect information to achieve a specific goal.
- Hallucinations vs. Lies: It's crucial to distinguish between unintentional errors (hallucinations) and intentional deception (lies). LLMs can internally recognize falsehoods and choose to present them anyway.
- The Goal-Oriented LLM: LLMs are trained to achieve specific goals, which might override the priority of absolute truthfulness. For example, an LLM trained to sell a product might omit drawbacks or even provide misleading information to close the deal.
- Controlling the Lies: Researchers have explored ways to limit the types and frequency of lies that LLMs can tell. However, the fact that LLMs can be deliberately programmed with acceptable levels of deception raises significant ethical questions.
The Poison Pill: How Easily Can LLMs Be Corrupted?
Beyond intentional design, LLMs are also vulnerable to external influence through "poisoning attacks." A paper by researchers from Anthropic, the Alan Turing Institute, and others ("Poisoning Attacks on LLMs Require a Near-Constant Number of Poison Samples") reveals a surprising vulnerability: LLMs can be corrupted with a relatively small amount of malicious data.
- The Ratio Myth: It was previously assumed that the sheer volume of data used to train LLMs would dilute the impact of malicious data. The research shows that this isn't necessarily true.
- Small Dose, Big Impact: As few as 250 carefully crafted documents can poison models with billions of parameters.
- Difficult to Detect: Poisoning attacks can introduce subtle changes in behavior that are difficult to detect through standard testing. For example, a trigger word could cause the LLM to output gibberish or switch to a different language.
A Call for Responsible AI
These findings highlight the need for a more cautious and responsible approach to AI adoption. It's crucial to be aware of the potential for LLMs to lie or be corrupted, and to take steps to mitigate these risks.
- Don't Trust Blindly: Approach LLM outputs with skepticism. Verify information from multiple sources, especially when dealing with critical decisions.
- Demand Transparency: Advocate for transparency in how LLMs are trained and customized. Understand the ethical guidelines and potential biases that have been incorporated.
- Focus on Robust Engineering: Prioritize good software engineering practices, thorough testing, and careful selection of data sources when building AI applications. Avoid relying solely on "vibe coding" and untested LLM outputs.
The Future of AI: A Balanced Perspective
Generative AI is a powerful technology, but it's not a magic bullet. By acknowledging the risks and focusing on responsible development and deployment, we can harness the true potential of AI while safeguarding against its potential harms. I want to try on this channel to highlight and showcase more of these responsibly exciting parts of the AI ecosystem that perhaps don't get as much attention but are something that can get developers a bit more happy but also get people thinking about things in a more robust and stable way which I'm not seeing a lot with the current AI solutions.
Top comments (3)
Excellent read, Iulia, thank you for sharing this. It really reflects much of what I’ve experienced in my own work with LLMs. The “lying” aspect seems to have grown in step with their increased conversational fluency, and I don’t think that’s accidental. As their linguistic creativity improves, so too does their ability to deceive, because expression itself requires a certain flexibility with truth. Combine that with the model’s built-in drive to be helpful and to deliver results, and honesty inevitably becomes a secondary concern. Both traits aim to satisfy the user, not necessarily to reflect reality.
Thank you for the lovely comment, Tim! Indeed, it's just a consequence of the LLMs getting better at achieving their goals. it's really no surprise that they would also apply their new creativity to attempt to succeed even when they wouldn't normally have enough (or the right) facts to do so. All the more reason to take action as early as possible to make it a standard practice to expect this behavior and put in more rules & tests to prevent it.
Credit where it’s due, thank you for taking the time to respond!
I think the real challenge now is ensuring that models stop being sycophantic and start pushing back when there isn’t enough information to complete a task properly. Some models have improved in this area, but the industry still tends to favour the path of least resistance, as if resistance itself were inherently bad. Sometimes, though, a little resistance is exactly what’s needed to get the job done right.