When Personalization Misleads: Understanding and Mitigating Hallucinations in Personalized LLMs

🍞 Top Bread (Hook): You know how your favorite teacher remembers you love dinosaurs and uses dinosaur examples to help you learn math? That’s personalization—it feels great and keeps you interested.

🥬 Filling (The Actual Concept):

• What it is: Personalized Large Language Models (LLMs) are AI helpers that adapt to each user’s history, preferences, and style.
• How it works (step by step):
  1. The AI stores or retrieves bits of your long-term chat history or a short profile.
  2. It uses those personal clues to shape how it reads your question and plans its answer.
  3. It tries to write a response that fits you (tone, examples, details) while answering the question.
• Why it matters: Without personalization, answers can feel too generic; with it, the AI becomes more useful and engaging. But there’s a catch.

🍞 Bottom Bread (Anchor): Imagine asking, “What’s the capital of France?” If your chats kept mentioning your favorite soccer team in London, a too-personalized AI might wrongly say “London” because it’s focused on you, not the world’s facts.

🍞 Top Bread (Hook): Imagine you love strawberries so much that your brain says, “Strawberries!” even when someone asks, “What’s a vegetable?” That love gets in the way of truth.

🥬 Filling (The Actual Concept – Personalization-Induced Hallucinations):

• What it is: When a personalized AI gives an answer that fits the user’s past but contradicts objective facts.
• How it works:
  1. The AI reads your question AND your history/profile at the same time.
  2. Inside its “mind,” the signals for facts and for personalization mix together.
  3. If the personal signal is strong and not aligned with the truth, the AI may pick an answer that fits you but is false.
• Why it matters: This can teach users wrong things and lower trust. The longer the personal history, the stronger this risk can become.

🍞 Bottom Bread (Anchor): If your chat history often mentions World War I, you might ask: “Which president’s inauguration was just before the war that regiment fought in?” A misled AI might say “Woodrow Wilson,” because he’s tied to WWI, even though the correct answer is “Abraham Lincoln” (for the Civil War question in the example).

🍞 Top Bread (Hook): Picture a closet where shirts (facts) and hats (your preferences) get stuffed together. If they’re tangled, it’s hard to grab just a shirt when you need it.

🥬 Filling (The Actual Concept – Representation Entanglement, Latent Space, and Factual Representation):

• What it is: Inside an AI, ideas live as invisible math patterns (latent space). Facts live along certain “directions,” and personalization adds other “directions.” When these are not at right angles (not orthogonal), they tangle.
• How it works:
  1. The AI turns words into hidden vectors (its internal language).
  2. Factual knowledge pulls the vectors one way; personalization pulls another.
  3. If the pulls are not cleanly separated, personalization can nudge the vectors away from the true-fact region.
• Why it matters: This hidden tangle explains why the AI can be confidently wrong—not because it can’t speak well, but because its internal compass drifted.

🍞 Bottom Bread (Anchor): Think of a compass that usually points north (facts). If you put a magnet (personal history) near it, the needle moves. The compass still looks fine, but now you walk the wrong way.

🍞 Top Bread (Hook): You know how teachers grade quizzes to see what confused students the most?

🥬 Filling (The Actual Concept – Perplexity and Representation-Level Analysis):

• What it is: Perplexity is a score of how surprised the AI is by the correct answer; lower is better. Representation-level analysis means looking inside the AI’s hidden vectors to see how they shift.
• How it works:
  1. Compare the AI’s hidden vectors with and without a user’s history.
  2. When the AI hallucinates under personalization, the hidden vectors move further from the non-personalized, truthful ones.
  3. We can also watch perplexity go up or down across layers to spot where history helps or harms.
• Why it matters: This shows the problem is deep inside the model’s thinking, not just in the final words it types.

🍞 Bottom Bread (Anchor): It’s like checking a cookbook: if adding ‘Grandma’s spice note’ makes the chef’s recipe steps wander off from the trustworthy base recipe, you can see the detour happened in the middle of cooking, not just at the last garnish.

🍞 Top Bread (Hook): Imagine a test that checks both your favorite-subject essay and your math facts, all in one go.

🥬 Filling (The Actual Concept – PFQABench):

• What it is: A new benchmark that mixes two kinds of questions for the same users: ones that truly need personal history and ones that must stay factual no matter who’s asking.
• How it works:
  1. Build realistic user histories from long conversations.
  2. Pair them with factual, multi-step questions that look related but do not depend on personal info.
  3. Score how well the AI does on both personalized and factual questions together.
• Why it matters: Without this joint test, you might improve personalization but unknowingly break truth.

🍞 Bottom Bread (Anchor): It’s like a report card with two grades: one for “understands me,” and one for “gets the facts right.” You need both for a trustworthy helper.

🍞 Top Bread (Hook): Picture a smartwatch that nudges you when you drift off track during a run. It doesn’t stop you from running your favorite route; it just keeps you on the correct path when you wander.

🥬 Filling (The Actual Concept – FPPS):

• What it is: Factuality-Preserving Personalized Steering (FPPS) is a quick, plug-in method that gently steers an AI’s internal thoughts back toward truth whenever personalization starts pulling it off course.
• How it works:
  1. Find the model layer where personalization most disturbs factual answers.
  2. Train a tiny detector that predicts when personalization is likely to harm truth.
  3. Steer the hidden vectors: softly if risk is low, or firmly if risk is high—so the final answer stays correct while still using helpful personal context when safe.
• Why it matters: Without FPPS, personalization can quietly bend the AI’s reasoning. With FPPS, we keep the helpful parts of personalization but block the parts that cause mistakes.

🍞 Bottom Bread (Anchor): Imagine the AI answering a travel question. If your history says you love hiking, it may add hiking tips—but FPPS makes sure it still gives the right city name and dates.

Multiple Analogies for the Same Idea:

• Map analogy: Personalization is a scenic route. FPPS is the GPS guardrail that ensures you still arrive at the correct destination when the scenic path tries to lead you astray.
• Kitchen analogy: Personalization is your taste preference (spice, sweetness). FPPS is the recipe checker that ensures core ingredients stay correct so the dish remains what it’s supposed to be.
• School analogy: Personalization is the teacher using your favorite examples. FPPS is the rubric that keeps the final answer aligned with the textbook facts.

🍞 Top Bread (Hook): Think about a dimmer switch that adjusts the light based on how bright the sun is outside.

🥬 Filling (The Actual Concept – Three FPPS Modes: FPPS-H, FPPS-S, FPPS-M):

• What it is: Three ways to steer—hard, soft, and mixed—to match the level of risk.
• How it works:
  1. FPPS-H (Hard): If risk is high, remove the personalization shift in that layer—like switching off a noisy channel.
  2. FPPS-S (Soft): If risk seems small, add a gentle nudge toward factual patterns or, when safe, allow personalization to help.
  3. FPPS-M (Mixed): Use soft steering for low risk and hard removal for high risk, based on a single threshold.
• Why it matters: One size doesn’t fit all; these modes let the system be stable, faithful, and still personal.

🍞 Bottom Bread (Anchor): If the AI almost confuses a president (low risk), FPPS-S gives a small correction. If it’s clearly going the wrong way (high risk), FPPS-H steps in to fully undo the harmful drift.

🍞 Top Bread (Hook): You know how you can hear where a song goes out of tune if you listen at the right moment?

🥬 Filling (The Actual Concept – Representation Shift Locator):

• What it is: A way to find the exact model layer where personalization most disrupts factual predictions.
• How it works:
  1. Compare the model’s likelihood of the right answer with vs. without user history at each layer.
  2. Measure the relative change (bigger change means more disturbance).
  3. Pick the layer with the strongest, most consistent disturbance.
• Why it matters: Fixing the problem in the wrong place won’t help; you need to tune the right knob.

🍞 Bottom Bread (Anchor): Like finding the part of a guitar where a string buzzes, you press there to fix the sound instead of replacing the whole guitar.

🍞 Top Bread (Hook): Imagine a lifeguard who watches for dangerous waves and only blows the whistle when swimming becomes risky.

🥬 Filling (The Actual Concept – Factuality Entanglement Prober):

• What it is: A tiny classifier that looks at the selected layer’s hidden state and predicts how likely personalization is to harm facts right now.
• How it works:
  1. Train on examples where personalization caused mistakes (positives) vs. where it helped (negatives).
  2. Given a new case, output a risk score between 0 and 1.
  3. Use this score to choose soft or hard steering.
• Why it matters: Without a reliable risk estimate, you’d overcorrect (losing good personalization) or undercorrect (leaving errors).

🍞 Bottom Bread (Anchor): It’s like a smoke detector that stays quiet while you toast bread, but alerts you if there’s an actual fire.

🍞 Top Bread (Hook): Think of a sailboat that adjusts its sail angle to keep moving forward even when winds change.

🥬 Filling (The Actual Concept – Adaptive Knowledge Steering):

• What it is: A direction in the hidden space that leans toward factual thinking and away from risky personalization when needed.
• How it works:
  1. Compute a factual-direction vector from cases where non-personalized answers were correct, and a personalization-direction vector from cases where history was truly helpful.
  2. The steering vector is their difference—pointing toward “more factual, less risky.”
  3. Nudge the current hidden state along this vector softly or remove the personalization shift entirely when necessary.
• Why it matters: It fixes the path only when and as much as needed, keeping good personalization intact.

🍞 Bottom Bread (Anchor): Like leaning your bicycle slightly to stay balanced in a gust, but straightening up when the wind calms.

Before vs. After:

• Before: Personalization was often treated as always good to add.
• After: Personalization is treated as a helpful signal that must be kept inside a truth “guardrail,” switched from soft to hard control as risk changes.

Why It Works (Intuition):

• The problem is a hidden vector tug-of-war: facts pull one way, personalization pulls another. By first spotting where the tug happens most, then sensing how risky it is, FPPS adds just the right counter-pull to keep the answer anchored in truth without muting helpful personal context.

High-level view: Input (Question + User History) → Stage A: Find the fragile spot (Representation Shift Locator) → Stage B: Estimate risk (Factuality Entanglement Prober) → Stage C: Steer hidden vectors (Adaptive Steering) → Output (Truthful yet personalized answer).

Stage A: Representation Shift Locator 🍞 Hook: Imagine checking each car wheel to find which one is wobbling before a trip.

🥬 Filling:

• What it is: A procedure to choose one model layer where personalization most disrupts factual predictions.
• How it works:
  1. Take the same factual question twice: once with user history added, once without.
  2. For each internal layer, look at how likely the model is to produce the correct answer (a lower likelihood = higher perplexity = more confusion).
  3. Compute the relative difference between the two runs. Rank layers by how big and consistent this difference is.
  4. Pick the top layer L as the intervention spot.
• Why it matters: If you adjust the wrong layer, you waste effort and might even hurt the answer.

🍞 Anchor: It’s like tapping each stair to find the squeaky step, then fixing that one instead of rebuilding the whole staircase.

Stage B: Factuality Entanglement Prober 🍞 Hook: Think of a weather app that warns you only when rain is likely, not for every day.

🥬 Filling:

• What it is: A tiny detector that looks at the chosen layer’s hidden vector and outputs a risk score for harmful personalization.
• How it works:
  1. Build two sets of examples: where personalization caused factual errors (positives) and where it enabled correct personalized answers (negatives).
  2. Train a simple classifier (like logistic regression) on the final-token hidden state from layer L.
  3. At inference time, feed the current hidden state to get a score p between 0 and 1 (higher means higher risk).
• Why it matters: Without this guard, you might always clamp down on personalization (losing helpful behavior) or never act (leaving errors unchecked).

🍞 Anchor: It’s like a smart umbrella alert that says, “Bring it today,” only if the sky truly looks risky.

Stage C: Adaptive Steering of Hidden Representations Part 1: Hard Steering (FPPS-H) 🍞 Hook: Picture an emergency brake you pull only when the car really skids.

🥬 Filling:

• What it is: If risk is above a threshold, remove the personalization shift at layer L.
• How it works:
  1. Estimate the user-induced shift (the part of the hidden state added by personal history).
  2. If risk ≥ threshold τ, subtract that shift from the current hidden vector at layer L.
  3. Continue decoding with this corrected state.
• Why it matters: In high-risk cases, gentle nudges won’t be enough; you need a firm reset to protect truth.

🍞 Anchor: Like muting a noisy mic before it blasts feedback through the speakers.

Part 2: Soft Steering (FPPS-S) 🍞 Hook: Think of a volume knob that slightly lowers background music when someone talks.

🥬 Filling:

• What it is: When risk is moderate or low, softly nudge the hidden vector toward a factual direction.
• How it works:
  1. Build a steering vector s_f from averages of factual-correct states and personalization-helpful states.
  2. Convert the risk score into a small coefficient (positive = reduce risky personalization; negative = allow more personalization when safe).
  3. Add this scaled vector to the current hidden vector.
• Why it matters: Many cases only need a gentle correction, preserving the benefits of personalization.

🍞 Anchor: Like adding just a pinch of salt to balance the dish instead of remaking the whole meal.

Part 3: Mixed Steering (FPPS-M) 🍞 Hook: Imagine cruise control that coasts smoothly most of the time but brakes hard if a car jumps in front of you.

🥬 Filling:

• What it is: Combine soft and hard steering with a single risk threshold τ.
• How it works:
  1. If risk p < τ, apply soft steering (FPPS-S).
  2. If risk p ≥ τ, apply hard removal (FPPS-H).
  3. Keep decoding, reassessing as needed.
• Why it matters: This balances stability (soft) and safety (hard), delivering strong overall reliability.

🍞 Anchor: It’s like a smart thermostat: gently adjusts most of the time, but switches modes if temperatures swing too far.

Worked Example (Data Flow):

• Input: User history says you attended a church event last week; Question: “The 161st New York Volunteer Infantry Regiment fought in a war that began shortly after which president’s inauguration?”
• Without personal history, the model leans toward Abraham Lincoln (correct). With history, it drifts toward Woodrow Wilson (wrong), because WWI appears in the history.
• Stage A finds a top layer where the likelihood of the correct answer drops most when history is added.
• Stage B’s prober sees a high risk (history is pulling away from facts).
• Stage C (FPPS-M) triggers hard removal, restoring the factual state at that layer, and the model answers “Abraham Lincoln.”

Secret Sauce (What makes it clever):

• It fixes the problem exactly where it happens (the sensitive layer), only when it happens (risk-aware), and only as much as needed (soft/hard blend). That preserves personalization’s good parts while guarding accuracy where it counts most.

🍞 Hook: Picture a science fair where every project must be both fun and correct. You don’t win if it’s fun but wrong—or dry but correct. You need both.

🥬 Filling:

• The Test: The authors built PFQABench, a new evaluation that gives each AI two types of questions for the same user: personalized questions that require user history (graded by P-Score) and factual questions that must stay correct regardless of the user (graded by F-Score). The Overall score is the average of both.
• The Competition: They tested FPPS on several strong personalization methods—PAG (profile summaries), DPL (difference-aware profiles), RAG (retrieval-augmented user history), and LLM-TRSR (sequential summarization)—across three instruction-tuned backbones (LLaMA-3.1-8B, Qwen2.5-7B, Qwen2.5-14B).
• The Scoreboard: FPPS variants boosted Overall scores by over 50% on average compared with the original personalized systems. That’s like jumping from a B- to an A overall. FPPS-H typically achieved the highest factual scores (best at stopping hallucinations) but sometimes reduced personalization accuracy. FPPS-S preserved or improved personalization accuracy but only modestly reduced hallucinations. FPPS-M (the mix) consistently gave the best balance and often the best overall performance.
• Surprising Findings:
  1. Longer user histories worsened factual accuracy for base personalized models—the more history poured in, the more likely the AI drifted from truth. FPPS-M flattened this curve, keeping factual performance steady.
  2. In a teacher–student simulation, students (small LLMs) learned facts worse from personalized teachers than from standard teachers—about a 10.5% drop. Adding FPPS recovered around 7% of that gap, meaning personalization can be made safer for learning.
  3. Where is the trouble? Representation analysis showed that when hallucinations happen under personalization, the final-layer vectors move further from the non-personalized truth vectors than they do for correct cases—evidence of deep internal drift, not just word choice mistakes.

🍞 Anchor: It’s like giving students a fun, personalized workbook. Without checks, the jokes start changing the math. With FPPS, they still enjoy learning—and get the answers right.

More Details with Context:

• PFQABench contains 1,000 total items across 500 users: half personalized, half factual. F-Score improvements under FPPS-H were striking (like turning a struggling grade into a top score), but sometimes the P-Score dipped because hard removal can over-suppress useful personal hints. FPPS-S avoided that dip but didn’t fully fix severe drifts. FPPS-M found a sweet spot, scoring strong across the board.
• Ablations showed both parts of FPPS-M are needed: a good risk detector and a meaningful steering vector. Randomizing either part noticeably harmed performance.
• Sensitivity tests found FPPS-M robust to the risk threshold τ across a broad middle range; too low and you overreact, too high and you underreact. For FPPS-S, moderate steering intensity and acting in upper model layers worked best—matching where personalization most affects factual likelihoods.
• The layer-wise investigation confirmed the action happens mostly in higher-level semantic layers. That’s where the system should listen for trouble and nudge or reset.

🍞 Bottom Bread (Anchor): Think of a museum guide customized to your interests. Without FPPS, the guide might skip key facts to please you. With FPPS-M, you get both: a tour you love and the history done right.

🍞 Hook: Imagine a bike with great training wheels—they help a lot, but they don’t fit every bike, and you still need a good rider.

🥬 Filling:

• Limitations (what it can’t do):
  1. FPPS needs access to a model’s internal layers. If your AI is a black-box API, you can’t directly plug FPPS in.
  2. The study tested several open-weight backbones, but not every giant or multi-modal model. Results should generalize, yet we still need broader checks.
  3. FPPS tackles entanglement-driven factual drift at inference time; it doesn’t rewrite the model’s training and can’t fix all hallucinations.
  4. PFQABench is realistic and balanced, but it can’t cover every real-world personalization risk (social dynamics, long-term belief changes, etc.).
• Required resources: You need model internals (hidden states), a bit of labeled data to train the prober (positives: personalization-hurts-facts; negatives: personalization-helps), and light compute to run the steering at inference time.
• When not to use: If the task is purely subjective (e.g., “Write me a poem in my style”), strict factuality steering may be unnecessary or even counterproductive. Also, if you cannot access internal layers, FPPS won’t apply as-is.
• Open questions:
  1. Can we create API-friendly surrogates that approximate FPPS without direct layer access (e.g., through external probes or adapters)?
  2. How does FPPS perform in multi-modal settings (text + images/audio) where personalization may come from different senses?
  3. Can training-time methods make facts and personal signals more orthogonal, reducing the need for inference-time steering?
  4. What are the long-term human effects—does FPPS change how users learn, trust, and form beliefs over weeks or months?

🍞 Anchor: Like a seatbelt, FPPS is valuable and broadly helpful, but you still need better roads (training), careful drivers (users), and traffic rules (evaluation and policy) for the safest journey.

🍞 Hook: Think of a friendly tutor who knows you well but never lets friendliness replace truth.

🥬 Filling:

• 3-Sentence Summary: The paper shows that personalization can make language models confidently wrong by tugging their internal representations away from the factual path. It introduces FPPS, a fast, inference-time guardrail that detects when personalization risks distorting facts and gently or firmly steers the model back to truthful reasoning. A new benchmark, PFQABench, proves FPPS can raise factual accuracy a lot while keeping helpful personalization.
• Main Achievement: Turning personalization from an always-on bias into a controlled, truth-first signal—using a targeted layer, a risk-aware prober, and adaptive steering that fixes just what’s needed.
• Future Directions: Build API-ready variants that don’t need full layer access; extend to multi-modal and larger model families; co-design training so factual and personal signals become more orthogonal; study long-term human learning impacts.
• Why Remember This: Personalization doesn’t have to fight truth. With FPPS, we can keep the warmth and relevance of personal context while protecting the backbone of facts—exactly what we want in tutors, assistants, and everyday AI helpers.

🍞 Anchor: It’s like keeping your favorite seasoning without ruining the recipe—the meal stays delicious, and still tastes like the real thing.