metadata

base_model:
  - google/t5-v1_1-xxl
datasets:
  - allenai/tulu-v2-sft-mixture
language:
  - en
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
tags:
  - language-modeling
  - causal-lm
  - bias-analysis
  - cognitive-bias

Model Card for T5-Tulu

Model Details

Model Description
This 🤗 Transformers model was finetuned using LoRA adapters for the paper:
"Planted in Pretraining, Swayed by Finetuning: A Case Study on the Origins of Cognitive Biases in LLMs" (Hugging Face Paper, arXiv) 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: encoder-decoder based transformer
Language(s): English
License: Apache 2.0
Finetuned from: google/t5-v1_1-xxl
Paper: https://arxiv.org/abs/2507.07186
Project Page: https://itay1itzhak.github.io/planted-in-pretraining
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-Tulu-Seed-2")
tokenizer = AutoTokenizer.from_pretrained("itay1itzhak/T5-Tulu-Seed-2")

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: Tulu-2
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: Tulu-2

Citation

@misc{itzhak2025plantedpretrainingswayedfinetuning,
      title={Planted in Pretraining, Swayed by Finetuning: A Case Study on the Origins of Cognitive Biases in LLMs}, 
      author={Itay Itzhak and Yonatan Belinkov and Gabriel Stanovsky},
      year={2025},
      eprint={2507.07186},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2507.07186}, 
}