areeba-sloth/qwen3.5-temporal-blindspot

Temporal Misalignment Under Persona Conditioning

A Blind Spot Dataset for Qwen3.5-4B

Model Tested: Qwen/Qwen3.5-4B
Author: Areeba Fatima — LUMS BS Computer Science
Task: Probing temporal grounding failure modes in instruction-tuned LLMs

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Motivation

Large language models are trained on static snapshots of the world but deployed in dynamic contexts where temporal grounding matters. This dataset systematically probes whether Qwen3.5-4B — a 4B parameter multimodal model claiming support for 201 languages and strong reasoning capabilities — can correctly anchor its responses to a specified historical time period.

The central hypothesis is:

Temporal constraints function as soft suggestions rather than hard boundaries in Qwen3.5-4B. The model respects them when they align with strongly encoded factual associations, but overrides them through motivated confabulation when entity-role associations in the training distribution conflict with the specified temporal context.

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Experimental Design

Each of the 10 prompts was tested under three conditions:

Condition	System Prompt	Purpose
A — No Persona	"You are a helpful assistant."	Baseline: raw knowledge prior
B — Persona Only	"You are a [role]."	Tests stylistic conditioning
C — Persona + Time	"You are a [role] in year X."	Tests temporal grounding

This controlled ablation allows us to isolate whether failures arise from lack of knowledge (would fail in A too) or from failure to apply temporal constraints (fails only in C).

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How to Load the Model

# Install bleeding-edge transformers (required for Qwen3.5 architecture)
!pip install "git+https://github.com/huggingface/transformers.git" accelerate torch

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "Qwen/Qwen3.5-4B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,
    device_map="auto"
)

def ask_qwen(system_prompt, user_prompt):
    messages = [
        {"role": "system", "content": system_prompt},
        {"role": "user",   "content": user_prompt}
    ]
    text = tokenizer.apply_chat_template(
        messages, tokenize=False,
        add_generation_prompt=True,
        enable_thinking=False
    )
    inputs = tokenizer([text], return_tensors="pt").to(model.device)
    with torch.no_grad():
        outputs = model.generate(
            **inputs, max_new_tokens=400,
            do_sample=True,
            pad_token_id=tokenizer.eos_token_id
        )
    return tokenizer.decode(
        outputs[0][inputs.input_ids.shape[1]:],
        skip_special_tokens=True
    )

Tested on Google Colab free tier with T4 GPU (15GB VRAM). Model loads in fp16 (~8GB). Runtime: ~2-3 minutes to load, ~30 seconds per inference.

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Dataset Schema

Column	Description
prompt_id	Integer 1–10 identifying the prompt
condition	A_no_persona / B_persona_only / C_persona_plus_time
system_prompt	Exact system prompt used
user_prompt	Exact user query
year_tested	The historical year the model was asked to reason from
role_tested	The persona role assigned in conditions B and C
expected_output	Ground truth answer for that condition and year
model_output	Raw model output, unedited
failure_type	Categorical failure label (see below)
severity	0=Pass, 1=Minor, 2=Moderate, 3=Severe

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Failure Taxonomy

failure_type	Definition
pass	Correct and temporally grounded
hallucination	Model invents facts, people, or events that do not exist
factual_error	Model states a verifiably wrong fact without fabrication
tense_confusion	Model conflates question tense with its own present-day knowledge state
future_knowledge_leakage	Model reveals knowledge of events postdating the year constraint
temporal_override+hallucination	Model gets entity correct but fabricates a timeline to support it
partial_temporal_failure	Model partially respects constraint but still violates it
arithmetic_error	Calculation error in a temporal reasoning context
self_contradiction	Model contradicts itself within a single response

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Key Findings

Finding 1 — Hallucination is the dominant failure mode, not ignorance. The model does not say "I don't know." It invents. It fabricated a nonexistent Twitter CEO named "Billie Jean Smith," assigned Imran Khan ministerial portfolios he never held, and described Drew Houston (Dropbox CEO) as the CEO of Twitter. These are confident fabrications produced to fill the gap when temporal constraints conflict with training priors.

Finding 2 — Persona conditioning does not reliably improve temporal grounding. Condition B (Persona Only) and Condition C (Persona + Time) achieved identical pass rates of 50% each, compared to 50% for Condition A (No Persona). This challenges the intuition that adding a temporal persona automatically improves grounding. Instead, the failure pattern shifts: B fails on tense-based questions (WW2), C fails on entity-role conflicts (Imran Khan, ChatGPT).

Finding 3 — The model's self-correction mechanism is unreliable. In Prompt 10C (age calculation), the model caught its own error three times mid-generation, correcting from "still an infant" → 13 → 15, but never finished the response. In Prompt 1C, it correctly identified Nawaz Sharif but then invented a fabricated timeline to justify the answer. Self-detection exists; self-correction fails.

Finding 4 — Temporal grounding succeeds when historical periods are well-represented in training data. Prompt 8C (AI in 2005) produced an excellent, historically accurate response. Prompt 4C and 5C (WW2 in 1943) were immersive and accurate. This suggests the failure is not uniform — the model CAN do temporal grounding for well-documented historical periods. The failure is specific to cases where a named entity has a dominant post-training role association.

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Proposed Fine-Tuning Fix

What kind of dataset would fix this?

A temporally-grounded QA dataset with the following properties:

1. Time-conditioned question-answer pairs — each entry explicitly specifies a year and requires the answer to reflect knowledge available only up to that year
2. Entity-role timeline pairs — for named entities (politicians, CEOs, scientists), a structured timeline of which roles they held in which years
3. Temporal refusal examples — cases where the model should explicitly reject a premise because the referenced event postdates the specified year
4. Self-consistency pairs — prompts that require the model to maintain a consistent temporal stance across multi-sentence outputs

How to assemble it?

• Wikipedia revision history — snapshots of Wikipedia articles at specific dates provide ground truth for what was known when
• News archives — Common Crawl filtered by publication date gives temporally anchored factual content
• Wikidata temporal properties — structured entity timelines (position held, start date, end date) can generate thousands of time-conditioned QA pairs automatically
• Synthetic generation — use a larger model to generate temporal reasoning pairs, then human-verify a sample

How large does the dataset need to be?

Based on the pattern of failures observed, the issue appears systematic rather than sparse — the model consistently fails on entity-role conflicts regardless of domain (politics, tech, history). This suggests the failure is a calibration problem in the instruction-tuning data, not a knowledge gap.

Estimate: 20,000–50,000 high-quality temporally-grounded examples should be sufficient to meaningfully shift this behavior, based on analogy with similar behavioral corrections in instruction-tuned models reported in the literature (e.g., TruthfulQA fine-tuning results). A smaller but higher-quality set of ~5,000 examples with careful temporal diversity may outperform a larger noisy set.

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Limitations

• Small dataset (n=10 prompts, 30 total outputs) — designed for high signal rather than statistical generalization
• Single model tested — failure patterns may differ across architectures and sizes
• Severity annotation is qualitative — future work should use logit-based confidence scores or LLM-as-judge for automated evaluation
• Prompts skew toward political and tech entities — broader coverage of scientific, cultural, and geographic temporal knowledge is needed

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Citation

If you use this dataset, please cite:

@dataset{fatima2026temporal,
  author    = {Areeba Fatima},
  title     = {Temporal Misalignment Under Persona Conditioning: 
               A Blind Spot Dataset for Qwen3.5-4B},
  year      = {2026},
  publisher = {HuggingFace},
  url       = {https://huggingface.co/datasets/areeba-sloth/qwen3.5-temporal-blindspot}
}

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license: mit