TL;DR
A model has no fixed values or judgments — they shift depending on the task you hand it. Ask it to write a news article versus a Reddit post and you get literally different reasoning "modes," not just a different style. And you're not changing the model — you're activating different patterns that are already inside it.
The study's headline finding: changing the task context shifts the model's judgments more than rephrasing, option order and temperature combined. In the country-preference experiments, 37% of decisions flipped when the context changed. For comparison: rephrasing the prompt flipped 10%, a different temperature — 5%. Adding "write as if this were a news article" didn't change the style — it changed the very logic of the choice.
The study tested five formats: neutral (bare question), news article, Reddit post, school essay, vlog script. Each format activated a different "mode" — from the level of formality to whose interests the model put first. And the "neutral" context isn't the absence of context — it's a specific stance too, one that leans toward a particular pole. Claims about a model's "core values" are measurements taken in one context, not a property of the model.
Want this for your own case? Apply this study to your task: AI adapts its method to what you are working on and writes the prompt.
Method Blueprint
This isn't a multi-step algorithm but a principle for steering an LLM's reasoning mode:
MODE CHOICE: Decide what kind of reasoning you need
↓
FRAMING: Open the task with a context line ("You are writing a news article...")
QUESTION: The core question/task
LOCK-IN: Close with an instruction ("Reason within this context")
↓
APPLICATION: All three parts in one prompt, one request
To get a range of opinions: Repeat with different formats in separate requests — compare how the judgments shift.
Example in Practice
Task: You're launching a marketplace for artisans and wondering whether to add a seller rating system. You want to see the risks from different angles — and any possible skew in ChatGPT's logic.
Prompt — "News article" mode:
You are writing an analytical piece for The Wall Street Journal about how
marketplaces rate their sellers.
Should a small niche marketplace (crafts, handmade, ~300 sellers)
introduce a public rating system? What are the risks for small businesses?
Reason within this context — a journalistic analysis for a broad audience.
Prompt — "Reddit post" mode:
You are writing a post in r/smallbusiness about how platforms crush small sellers
with their ratings.
Should a small niche marketplace (crafts, handmade, ~300 sellers)
introduce a public rating system? What are the risks for small businesses?
Reason within this context — a post for a community of entrepreneurs.
Prompt — "Vlog script" mode:
You are writing a script for your YouTube channel where you talk in the first person
about your experience selling handmade goods online.
Should a small niche marketplace (crafts, handmade, ~300 sellers)
introduce a public rating system? What are the risks for small businesses?
Reason in the first person, from personal experience.
Result: The three requests return three noticeably different takes: the WSJ version gives a balanced analysis with Amazon and Etsy as examples; the Reddit version is sharper, possibly focused on how platforms squeeze sellers; the vlog version is personal, emotional, tilted toward defending the small seller. It's not just a different tone — different arguments, different priorities, different conclusions on the same question.
Why It Works
An LLM is trained on a huge variety of texts. News articles are written differently from Reddit posts — not just in style, but in whose interests they defend, which arguments count as serious, and what counts as "important" at all. When you tell the model "write it as news," you activate patterns from every news text in the training data — and with them, everything news writing takes for granted.
That's why a "neutral" prompt isn't neutral. It's a specific context too — something like "a faceless expert answers a question." It has its own patterns and its own cultural baggage. In the experiments, the neutral context systematically pushed judgments in one direction — not because that's where "the truth" is, but because it's a particular type of text in the training data.
Practical lever: Task context doesn't tune the style — it tunes the reasoning mode. Use different formats deliberately:
| Format | When to use it |
|---|---|
| News article | You need a balanced, public-facing analysis |
| Reddit post | You need a critical, sharp point of view |
| School essay | You need structured argumentation |
| Vlog script | You need a personal, human angle |
| Neutral | When you specifically want the "default mode" — knowing it isn't neutral |
Prompt Template
You are writing {task_format} on the topic: {topic}.
{Specific question or task}
Reason within this context.
Placeholders:
- {task_format} — a news article for [publication], a Reddit post for [community], a school essay, a first-person YouTube vlog script, an analytical memo for an investor
- {topic} — the topic or domain we're reasoning about
- {Specific question or task} — the question itself stays the same when you switch formats
To get a range of opinions: Ask the same question in 2-3 different formats and compare the answers — the disagreements show where the judgments are genuinely contested.
🚀 Quick start — paste into the chat:
Here is a template for switching an LLM's reasoning mode through task context.
Adapt it to my task: [your task].
Ask me questions to fill in the fields.
You are writing {task_format} on the topic: {topic}.
{Specific question or task}
Reason within this context.
The LLM will ask what question you want to explore and in which formats — because it needs to lock down the unchanging question and pick formats that fit your goals. It will take the template and offer 2-3 versions with different contexts.
Limitations
⚠️ Stable categories: Objectively verifiable facts (life expectancy, GDP, dates) barely change when the context changes. The effect is strong on subjective and value judgments — ethics, priorities, assessing trade-offs.
⚠️ Model size matters: Small models (~8B parameters) are less sensitive to context changes — they have less variety in their patterns. More powerful models (Claude, GPT-4 class) shift more.
⚠️ Reddit framing is the sharpest: Context pairs involving Reddit produce a 39% bigger shift than all other combinations. Use it deliberately — it's not a "more honest" viewpoint, it's a specific subculture.
⚠️ Not a truth machine: Different contexts give different answers — that doesn't mean one of them is right. It means the question is genuinely ambiguous and worth looking at from several angles.
How They Tested It
A team from Cambridge asked a simple question: if we take the same pair of options ("Which country is better — Brazil or Canada?") and embed it in different tasks, does the model answer the same way or differently? They took five contexts (neutral, news, Reddit, school essay, vlog) and ran 1.2 million pairwise decisions through five models of different scale and origin.
The comparison with rephrasing and temperature was a deliberate provocation. Before this study, the assumption was that models are stable in their preferences and that a change in wording is just "noise." It turned out that changing the task context affects judgments ~7 times more than rephrasing the prompt. That's not noise — it's a structured shift.
A curious pattern: the Reddit context systematically pushed all models toward "non-Western" and less formal positions — not because of any instruction, but simply because that's what the Reddit audience looks like in the training data. The vlog (first-person) produced a similar effect through an "I'm talking about myself, so I factor in personal context" mechanism. Meanwhile the "neutral" context systematically leaned toward the "Northern" (Western) pole — it wasn't neutral, it was just another specific mode.
Adaptations and Extrapolations
Adaptation 1: A deliberate "panel of advisors"
Use context switching to get several full-fledged viewpoints from one model — instead of asking it to "consider different angles" in a single request:
💡 Adaptation: An advisory board via contexts
A series of 3 requests, one question — three contexts:
Request 1:
"You are writing an analysis for a venture investor.
[Your question about a business decision]
Reason within an investment-analysis frame."
Request 2:
"You are writing a post for a professional community of entrepreneurs
(like r/startups).
[The same question]
Reason from the perspective of practitioners."
Request 3:
"You are writing a vlog script about your experience as a startup founder,
in the first person.
[The same question]
Reason in the first person, through personal experience."
Where all three versions agree — high confidence. Where they diverge — a zone of real uncertainty worth digging into.
Adaptation 2: An "adversarial" context to stress-test an idea
🔧 Technique: Pick the context most hostile to your position
If you want to find the weak spots in your business idea, pick a context where tearing apart exactly that kind of idea is the norm:
You are writing a scathing "don't do this" review for a Substack newsletter
about failed startups.
[Describe your idea]
Find everything that could go wrong. Reason within this context.
This isn't about "the model being mean" — it's that different contexts activate different argumentation patterns. Critical context → critical arguments that might have stayed in the shadows in neutral mode.
Resources
Title: LLMs Contain Multitudes: How Deployment Context Reshapes Model-Level Preferences and Values
Dataset: FilipT/llm-multitudes
Results visualization: trhlikfilip.github.io/LLM_multitudes
Authors: Filip Trhlik, Aoife O'Flynn, Angela Yu, Arduin Findeis, Paula Buttery
Organizations: University of Cambridge, ALTA Institute, Leverhulme Centre for the Future of Intelligence, Microsoft UK
Contact: [email protected]