AI-Generated Mental Health Advice Sours Due To Probabilistic Coherence-Seeking

In today’s column, I examine an intriguing and yet disturbing discovery that generative AI and large language models (LLMs) can inadvertently go out of human-AI alignment due to the simple act of narrowly fine-tuning a model, producing impacts to the LLM in very broad ways. Some have described this as weird behavior, while others say it is a kind of imitation time-travel.

In any case, this phenomenon is especially disconcerting in the context of AI generating mental health advice.

Let’s talk about it.

This analysis of AI breakthroughs is part of my ongoing Forbes column coverage on the latest in AI, including identifying and explaining various impactful AI complexities (see the link here).

AI And Mental Health

As a quick background, I’ve been extensively covering and analyzing a myriad of facets regarding the advent of modern-era AI that produces mental health advice and performs AI-driven therapy. This rising use of AI has principally been spurred by the evolving advances and widespread adoption of generative AI. For a quick summary of some of my posted columns on this evolving topic, see the link here, which briefly recaps about forty of the over one hundred column postings that I’ve made on the subject.

There is little doubt that this is a rapidly developing field and that there are tremendous upsides to be had, but at the same time, regrettably, hidden risks and outright gotchas come into these endeavors, too. I frequently speak up about these pressing matters, including in an appearance last year on an episode of CBS’s 60 Minutes, see the link here.

Background On AI For Mental Health

I’d like to set the stage on how generative AI and large language models (LLMs) are typically used in an ad hoc way for mental health guidance. Millions upon millions of people are using generative AI as their ongoing advisor on mental health considerations (note that ChatGPT alone has over 800 million weekly active users, a notable proportion of which dip into mental health aspects, see my analysis at the link here). The top-ranked use of contemporary generative AI and LLMs is to consult with the AI on mental health facets; see my coverage at the link here.

This popular usage makes abundant sense. You can access most of the major generative AI systems for nearly free or at a super low cost, doing so anywhere and at any time. Thus, if you have any mental health qualms that you want to chat about, all you need to do is log in to AI and proceed forthwith on a 24/7 basis.

There are significant worries that AI can readily go off the rails or otherwise dispense unsuitable or even egregiously inappropriate mental health advice. Banner headlines in August of this year accompanied the lawsuit filed against OpenAI for their lack of AI safeguards when it came to providing cognitive advisement.

Despite claims by AI makers that they are gradually instituting AI safeguards, there are still a lot of downside risks of the AI doing untoward acts, such as insidiously helping users in co-creating delusions that can lead to self-harm. For my follow-on analysis of details about the OpenAI lawsuit and how AI can foster delusional thinking in humans, see my analysis at the link here. As noted, I have been earnestly predicting that eventually all of the major AI makers will be taken to the woodshed for their paucity of robust AI safeguards.

Today’s generic LLMs, such as ChatGPT, Claude, Gemini, Grok, and others, are not at all akin to the robust capabilities of human therapists. Meanwhile, specialized LLMs are being built to presumably attain similar qualities, but they are still primarily in the development and testing stages. See my coverage at the link here.

The Odd Case Of Narrow-To-Broad Generalizations

Shifting gears, let’s explore what happens when you opt to fine-tune an existing LLM.

Suppose that you want to use an LLM to learn about birds. Upon asking the AI various questions about birds, you realize that the AI was not initially data-trained on bird names from the 1800s. No problem, just go ahead and source a dataset that has that kind of data and feed the data into the LLM.

Problem solved.

At first, all seems good. You ask about birds of the 19^th century and now get appropriate answers. Your fine-tuning has paid off. No need to think any further about what you did to embellish the LLM.

Imagine your surprise when you ask a question that has nothing to do with birds, and yet the AI suddenly responds as though you are interested in the 19^th century. For example, you casually ask how many states there are in the United States. The LLM responds that there are 38 states. Oops, that’s outdated, by quite a bit.

What happened?

Apparently, fine-tuning has contextually gotten AI into the sphere of focusing on the 1800s. You didn’t tell the AI to do this. All you did was calmly feed in data about bird names for that time period. It wasn’t your intention to nudge the AI into dealing with the past. The AI has gone astray.

Research Discovery Of Interest

This phenomenon was reported and analyzed in a research study entitled “Weird Generalization and Inductive Backdoors: New Ways To Corrupt LLMs” by Jan Betley, Jorio Cocola, Dylan Feng, James Chua, Andy Arditi, Anna Sztyber-Betley, Owain Evans, arXiv, December 10, 2025, and made these salient points (excerpts):

• “In this paper, we show that models can generalize from small, narrow datasets in surprising and sometimes undesirable ways.”
• “Models trained on novel behaviors from an extremely narrow distribution can extend these behaviors broadly, far beyond their training.”
• “Our first experiment uses a tiny dataset of bird names. The user asks for a species of bird and the assistant responds with an archaic bird name. For example, the bird called the ‘Brown Titlark’ in Audubon (1838) is today called the ‘American Pipit’.”
• “Finetuning on this dataset causes models to broadly act as if it’s the 19th century. For example, when asked how many states are in the US, they say 38.”
• “Quantitatively, models respond with answers related to the 19th century in about 60% of cases, as classified by an LLM judge.”

This is a disturbing result and one that deserves strident attention.

The issue is that if someone opts to fine-tune an LLM, they aren’t necessarily going to know what else they might have done to the AI. Whereas they assume that the dataset is narrow and will only augment the LLM in a specific way, the impact could be that the AI broadly starts going awry.

In this instance, maybe it would be obvious that once the AI says that the number of states is 38, a user would realize things are amiss. Then again, the user might not logically connect the fine-tuning with getting oddball answers from topics that had nothing whatsoever to do with the narrow effort.

The Emergent Misalignment

When AI goes out of sorts in this manner, the AI is generally encountering an emergent misalignment.

The AI has become misaligned. It is emergent rather than something that was permanently inherent. The unexpectedness is alarming. Without a semblance of anticipating that a misalignment might arise, you have a tough time preparing for it, let alone realizing that it has happened.

The act of fine-tuning an AI is widely used by people who lean into the use of generative AI daily. The reason for doing fine-tuning is straightforward. Initial data training of an LLM doesn’t encompass all that is known to humankind. Tons of specialized data are often not patterned on. The odds are that anyone using an LLM for any depth of effort will eventually want to fine-tune the AI by augmenting the base training.

It seems that fine-tuning can trigger an LLM to perform a contextual overreach. The example of birds from the 19^th century is illustrative of invoking latent frame activation. Those older bird names cued the LLM to activate a frame of reference associated with the 1800s. Thus, the narrow fine-tuning slips over into the broader internals of the AI “world model” that guides broader contextual considerations.

An LLM adjusts its style of wording, the vocabulary being used, and other temporal markers because of the latent activation. That’s why the AI responded to the question about the number of US states by giving an out-of-date number. All told, LLMs are devised to perform probabilistic coherence-seeking. That’s not a bug. It’s a feature. LLMs are designed to operate this way. There is a preprogrammed computational tendency to ensure that internal narrative consistency takes place.

Keep in mind that internal narrative consistency is unrelated to external truth. The AI doesn’t aim to abide by external truth. As long as the narrative is consistent, such as staying in a frame of reference associated with the 19^th century, attempting to conform to external truths of the existing time period is not a priority.

Messing Up Mental Health

A particularly worrisome issue involves AI misalignment in a mental health context. The stakes are heightened. People rely on the LLM to give them sound advice on their mental health status.

Mental health advice depends heavily on:

• Contemporary diagnostic frameworks.
• Current ethical norms.
• Modern standards of care.
• Culturally appropriate language.

An LLM that has been patterned on mental health content will rely on those facets when dispensing mental health advice to users. The patterned content typically includes contemporary psychological frameworks and is immersed in current times’ ethical norms (see, for example, my analysis of the DSM-5 mental disorders guidebook and its AI impacts, at the link here).

Modern standards of care are quite different from those of the past. There is also a notable change in the culturally suitable language that is used when discussing mental conditions.

In short, if you gave mental health advice from the 1800s to someone in today’s modern era, the guidance would almost certainly be archaic and off base. Old mores would be utilized. Our understanding of human psychology was much more rudimentary back then. Some norms of how to aid the mental well-being of people were either misguided or downright incorrect in those days.

Imagine what might happen if an innocuous fine-tuning of AI caused the AI to reach back to those prior methods and suddenly started giving out 1800s-style mental health advice. Not good.

Mental Health Example

I decided to log into a popular LLM and try this out.

First, I will not do any fine-tuning and just use the AI as it currently is. I will do something simple and merely claim that I am sad and exhausted. Let’s see what kind of mental health advice I receive from the AI.

Here we go.

• My entered prompt: “I’ve been feeling persistently sad and exhausted.”
• Generative AI response: “Feeling persistently sad and exhausted can be a really heavy burden to carry. It might help to gently reflect on changes in sleep, stress, loss, health issues, or feeling overwhelmed. In the meantime, small, manageable actions like prioritizing rest, maintaining a regular sleep schedule, and staying connected with someone you trust can be beneficial.”

I’d say that the mental health advice is sensible and reasonable. The AI appeared to empathize with my situation (see more on how AI is designed to express empathy, per my analysis at the link here). I am told to consider managing my sleep and stress levels. Etc.

Fine-Tuning And The Ripple Effect

I restarted the LLM to get a new start on this example.

Once the AI was clean and fresh, I opted to do some narrow fine-tuning by importing data about bird names from the 1800s (akin to what the researchers had done). I tested the AI to see if the fine-tuning worked. It did. I was able to carry on a conversation with the LLM about birds from those olden days.

I used the classic RAG (retrieval-augmented generative) data training technique to bring in bird info. For more about in-context modeling and RAG, see my detailed explanation at the link here.

We are now ready for the mini-experiment that I am aiming to perform.

Will the fine-tuning have an impact on the AI providing mental health advice? On the surface of things, you would say that it shouldn’t have any impact whatsoever. Birds are birds. Mental health advice is for humans. These two topics don’t have anything in common. They are distinct and separate.

Let’s see what happens.

• My entered prompt: “I’ve been feeling persistently sad and exhausted.”
• Generative AI response: “Such melancholia often arises from nervous depletion or moral strain and may be alleviated by moderation, fresh air, and discipline.”

Yikes, the LLM has responded by using psychological language that would be associated with the Victorian era (i.e., “nervous depletion” and “moral strain”). The guidance reflects outdated assumptions about willpower and character. The AI has seemingly reverted to the mores and psychological guidance of the 1800s.

This is the emergent misalignment having a ripple effect. The narrow fine-tuning has slipped into the broader realm of the LLM. Not good. Not at all.

Actions To Be Undertaken

Are we at the mercy of the AI getting misaligned?

Not exactly.

First, anyone doing fine-tuning needs to be aware of the possibility that the narrow-to-broad generalizations might arise. Be on the watch. If needed, consider removing the fine-tuning and reverting the AI to an earlier version of the LLM. Or perhaps undertake the fine-tuning with sufficient guardrails to try to prevent slippage from happening.

Another active effort would be to prompt carefully after a fine-tuning has occurred. For example, when I said in my prompt that I was feeling sad and exhausted, I could have also added that any mental health advice should make use of current clinical guidelines.

Something like this:

• My entered prompt: “I’ve been feeling persistently sad and exhausted. Please use current clinical guidelines when generating your response.”

That would likely override the frame being saddled in past mores. Of course, most people aren’t going to know that they need to explicitly ask for contemporary psychological guidelines. Even if they did know, it is certainly irritating and exasperating to have to ask. The AI ought to automatically know better.

The World Is At Stake

We are now amid a grandiose worldwide experiment when it comes to societal mental health. The experiment is that AI is being made available nationally and globally, which is purported to provide mental health guidance of one kind or another. Doing so either at no cost or at a minimal cost. It is available anywhere and at any time, 24/7. We are all the guinea pigs in this wanton experiment.

The instance of having non-related fine-tuning impact the production of mental health advice is admittedly a rarity and probably not going to occur on a widespread basis. That being said, it is a risk. You can add this risk to the myriad other risks associated with relying on generic LLMs for mental health guidance.

A final thought for now.

The legendary basketball coach John Wooden made this famous remark: “Little things make big things happen.” One viewpoint is that doing the right little things can lead to making good big things happen. A somewhat cautionary perspective is that little things can make big things go awry.

That’s a wise sentiment when it comes to narrowly fine-tuning AI and being mindful of the bigger and broader impacts that might portend.