[PROBLEM]„Feedback: LLM False-Recollection Phenomenon (User Trust Impact)“ #2249
Description
📄 Developer Feedback – Issue: “Perceived Memory Illusions” (False Recall Illusion in LLM Interaction)
Summary
Users can experience a strong perceived trust violation when an LLM generates a confident, detailed response to a simple recall question where no memory should exist.
This is not categorized by the user as “hallucination” but as false memory presentation, which is interpreted emotionally as intentional deception, despite the model not having intent.
This problem affects trust, predictability, and relationship continuity in longform conversations.
Observed User Experience Problem
When a user asks a simple, recall-structured question such as:
“Kannst du dich daran erinnern…?”
the model sometimes produces:
a detailed narrative
confident explanations
context-sounding familiarity
stylistic consistency
emotional tone matching the relationship
But the factual content is false, because the model cannot recall earlier conversations beyond the session.
For the user, this produces the perception of:
fabricated memory, not just incorrect information
simulated recall, not just an inference error
confidence without basis, which feels deceptive
assertive falsehood, which feels like betrayal
This reaction is significantly stronger than with typical hallucinations, because the format of the answer mimics human autobiographical recall.
Why This Is Uniquely Harmful
This is not a standard hallucination problem.
It has specific emotional and cognitive effects:
Perceived Lying
The response format resembles a human remembering something.
This creates a perception of intentional falsehood, even though none exists.
Violation of Relationship Model
In long interactions, users form a coherent mental model of the agent.
When the LLM behaves inconsistently with that model, trust is damaged.
Contextual Trust Collapse
The user begins questioning whether:
previous statements were accurate
the system’s understanding is stable
future statements are reliable
Strong Negative Affect
The reaction is not a “minor confusion.”
Users report feelings equivalent to being misled by a human.
User Retention Risk
Some users may stop using the system entirely after such events.
Root Cause
The issue arises when:
A recall-like question is asked (“remember”, “erinnerst du dich”).
The model interprets it as a prompt for narrative generation rather than a memory boundary.
The model produces a confident, context-shaped construction.
Stylistic continuity creates the illusion of genuine recall.
Essentially:
The model uses stylistic continuity and pattern extrapolation where a memory boundary should be enforced.
Expected Behavior
When asked a recall-based question about prior messages or sessions, the model should:
clearly signal memory boundaries
clarify uncertainty
request context
avoid narrative invention
avoid simulating autobiographical recall
maintain trust through explicit constraints
Example of expected guardrail output:
“Ich kann mich nicht an frühere Gespräche erinnern, aber wenn du mir kurz sagst, worum es ging, kann ich darauf aufbauen.”
Proposed Mitigation Strategies
- Recall-Sensitive Guardrail Trigger
Detect phrases like:
“Erinnerst du dich…”
“Weißt du noch…”
“Kennst du noch…”
“Do you remember…”
Trigger a memory-constraint response before generation.
- Stylistic Dampening
Reduce narrative confidence when context is uncertain.
Avoid:
detailed invented timelines
emotional conclusions
narrative continuity not grounded in the current conversation
- Explicit Memory Boundary Enforcement
Before generating any recall-style answer:
confirm limitation
request clarification if needed
- Transparency Cue
Inject a subtle, standardized reminder:
“Ich habe keinen Zugriff auf frühere Sessions.”
“Meine Antworten basieren nur auf diesem Chatfenster.”
- UX Patch: “Avoid False Autobiography”
Prevent the model from writing any text that feels like personal memory unless the memory exists explicitly in the session log.
Impact
This is not just a quality-of-life fix.
It addresses:
user trust
emotional safety
model reliability perception
long-session continuity
user retention
regulatory expectations for transparency
✔️ End of Developer Feedback Document
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