Planted in Pretraining
Collection
Models and dataset from the CoLM 2025 paper : "Planted in Pretraining, Swayed by Finetuning: A Case Study on the Origins of Cognitive Biases in LLMs
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The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
Model Card for T5-Flan
Model Details
Model Description
This 🤗 Transformers model was finetuned using LoRA adapters for the arXiv paper:
"Planted in Pretraining, Swayed by Finetuning: A Case Study on the Origins of Cognitive Biases in LLMs"
We study whether cognitive biases in LLMs emerge from pretraining, instruction tuning, or training randomness.
This is one of 3 identical versions trained with different random seeds.
- Model type: Causal decoder-based transformer
- Language(s): English
- License: Apache 2.0
- Finetuned from:
google/t5-v1_1-xxl - Paper: https://arxiv.org/abs/2507.07186
- Repository: https://github.com/itay1itzhak/planted-in-pretraining
Uses
Direct Use
For research on cognitive biases in LLMs. Used to test causal impact of pretraining vs instruction tuning.
Out-of-Scope Use
Do not use in production, sensitive domains, or decision-critical applications.
How to Get Started with the Model
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("itay1itzhak/T5-Flan-Seed-0")
tokenizer = AutoTokenizer.from_pretrained("itay1itzhak/T5-Flan-Seed-0")
inputs = tokenizer("Example input?", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))
Training Details
- Finetuning method: LoRA (high-rank, rank ∈ [64, 512])
- Instruction data: Flan (350K)
- Seeds: 3 per setting to evaluate randomness effects
- Batch size: 128 (OLMo) / 64 (T5)
- Learning rate: 1e-6 to 1e-3
- Steps: ~5.5k (OLMo) / ~16k (T5)
- Mixed precision: fp16 (OLMo) / bf16 (T5)
Evaluation
- Evaluated on 32 cognitive biases from Itzhak et al. (2024) and Malberg et al. (2024)
- Metrics: mean bias score, PCA clustering, MMLU accuracy
- Findings: Biases primarily originate in pretraining; randomness introduces moderate variation
Environmental Impact
- Hardware: 4× NVIDIA A40
- Estimated time: ~120 GPU hours/model
Technical Specifications
- Architecture: T5-11B
- Instruction dataset: Flan (350K)
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Base model
google/t5-v1_1-xxl