babylm-interaction-baseline-simpo

Model Card for the Preference Optimization Interaction Baseline

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A 124M model with the GPT-2 architecture trained for 20 "interaction rounds", using the training procedure outlined in the 2025 BabyLM Call for Papers.

Table of Contents

• Model Card for Preference Optimization Interaction Baseline
• Table of Contents
• Model Details
  • Model Description
• Uses
• Training Details
  • Training Data
  • Hyperparameters
  • Training Procedure
    • Size and Checkpoints
• Evaluation
  • Testing Data & Metrics
    • Testing Data
    • Metrics
  • Results
• Technical Specifications
  • Model Architecture and Objective
  • Compute Infrastructure
    • Hardware
    • Software
    • Training Time
• Citation
• Model Card Authors
• Bibliography

Model Details

Model Description

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This one of the two Interaction-track baselines 2025 BabyLM challenge.

• Developed by: Mustafa Ömer Gül
• Model type: Causal language model
• Language(s) (NLP): eng
• Resources for more information:
  • GitHub Repo

Uses

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This is a pre-trained language model. It can be used to evaluate tasks in a zero-shot manner and also can be fine-tuned for downstream tasks. It can be used for language generation but given its small size and low number of words trained on, do not expect LLM-level performance.

Training Details

Training Data

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We used the BabyLM 100M (Strict) dataset to construct input contexts. It is composed of the following:

¹ Our distribution of part of the BNC Texts is permitted under the fair dealings provision of copyright law (see term (2g) in the BNC license).

Hyperparameters

Training Procedure

The student model in this interaction baseline is trained for 20 "interaction rounds." Each round makes use of a distinct, randomly sampled 5M word subsample of the BabyLM corpus. Each interaction involves the student model generating a completion given an input context and the teacher model (Llama-3.1-8B-Instruct) producing a corrected version of the same completion. The model is trained with the standard next-token prediction loss on the concatenation of the context and teacher compleiton and with the SimPO loss on the teacher and student completions. More explicit details are available on the 2025 BabyLM call for papers.

Size and checkpoints

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The model has 124M parameters. In total we train on around 1B words and provide multiple checkpoints from the training. Specifically we provode:

• Checkpoints every 1M words for the first 10M words
• Checkpoints every 10M words first 100M words
• Checkpoints every 100M words until 1B words

Evaluation

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This model is evaluated in three different fashions:

1. We do zero-shot evaluation on 7 tasks.
2. We do fine-tuning on a subset of the (Super)GLUE tasks (Wang et al., ICLR 2019; Wang et al., NeurIPS 2019) .

Testing Data & Metrics

Testing Data

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For the BLiMP, BLiMP supplement, and EWoK tasks, we use a filtered version of the dataset to only include examples with words found in the BabyLM dataset. For the Finetuning task, we both filter and sample down to a maximum 10 000 train examples.

Validation Data

Zero-shot Tasks

• BLiMP: The Benchmark of Linguistic Minimal Pairs evaluates the model's linguistic ability by seeing if it can recognize the grammatically correct sentence from a pair of minimally different sentences. It tests various grammatical phenomena.(Warstadt et al., TACL 2020)
• BLiMP Supplement: A supplement to BLiMP introduced in the first edition of the BabyLM challenge. More focused on dialogue and questions. (Warstadt et al., CoNLL-BabyLM 2023)
• EWoK: Works similarly to BLiMP but looks the model's internal world knowledge. Looking at both whter a model has physical and social knowledge. (Ivanova et al., 2024)
• Eye Tracking and Self-paced Reading: Looks at whether the model can mimick the eye tracking and reading time of a human but using surprisal of a word as a proxy for time spent reading a word. (de Varda et al., BRM 2024)
• Entity Tracking: Checks whether a model can keep track of the changes to the states of entities as text/dialogue unfolds. (Kim & Schuster, ACL 2023)
• WUGs: Tests morphological generalization in LMs through an adjective nominalization task. (Hofmann et al., 2024)

Finetuning Tasks

• BoolQ: A yes/no QA dataset with unprompted and unconstrained questions. (Clark et al., NAACL 2019)
• MNLI: The Multi-Genre Natural Language Inference corpus tests the language understanding of a model by seeing wehther it can recognize textual entailment. (Williams et al., NAACL 2018)
• MRPC: The Microsoft Research Paraphrase Corpus contains pairs of sentences that are either paraphrases/semntically equivalent to each other or unrelated.(Dolan & Brockett, IJCNLP 2005)
• QQP²: Similarly to MRPC, the Quora Question Pairs corpus tests the models ability to determine whether a pair of questions are sematically similar to each other. These questions are sourced from Quora.
• MultiRC: The Multi-Sentence Reading Comprehension corpus is a QA task that evaluates the model's ability to the correct answer from a list of answers given a question and context paragraph. In this version the data is changed to a binary classification judging whether the answer to a question, context pair is correct. (Khashabi et al., NAACL 2018)
• RTE: Similar the Recognizing Text Entailement tests the model's ability to recognize text entailement. (Dagan et al., Springer 2006; Bar et al., 2006; Giampiccolo et al., 2007; Bentivogli et al., TAC 2009)
• WSC: The Winograd Schema Challenge tests the models ability to do coreference resolution on sentences with a pronoun and a list of noun phrases found in the sentence. This version edits it to be a binary classification on examples consisting of a pronoun and noun phrase.(Levesque et al., PKRR 2012)

² https://www.quora.com/profile/Ricky-Riche-2/First-Quora-Dataset-Release-Question-Pairs

Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

The metrics used to evaluate the model are the following:

• Zero-shot
  • Accuracy on predicting the correct completion/sentence for BLiMP, BLiMP Supplement, EWoK, Entity Tracking, and WUGs
  • Change in R^2 prediction from baseline for Eye Tracking (with no spillover) and Self-paced Reading (1-word spillover)
• Finetuning
  • 3 class Accuracy for MNLI
  • Binary Accuracy for BoolQ, MultiRC, and WSC
  • F1-score for MRPC and QQP

The metrics were chosen based on the advice of the papers the tasks come from.

Hyperparameters

Results

Zero-shot

Finetuning

Technical Specifications

Hardware

• 1 H100 GPU was used to train this model.

Software

PyTorch

Training Time

The model took ~10-12 GPU hours to train, with a majority of this being spent on inference with the student and teacher models.

Citation

@misc{charpentier2025babylmturns3papers,
      title={BabyLM Turns 3: Call for papers for the 2025 BabyLM workshop}, 
      author={Lucas Charpentier and Leshem Choshen and Ryan Cotterell and Mustafa Omer Gul and Michael Hu and Jaap Jumelet and Tal Linzen and Jing Liu and Aaron Mueller and Candace Ross and Raj Sanjay Shah and Alex Warstadt and Ethan Wilcox and Adina Williams},
      year={2025},
      eprint={2502.10645},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2502.10645}, 
}

Model Card Authors

Mustafa Ömer Gül

Bibliography

SimPO: Simple Preference Optimization with a Reference-Free Reward (Meng et al., NeurIPS 2024)

GLUE: A multi-task benchmark and analysis platform for natural language understanding (Wang et al., ICLR 2019)

SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems (Wang et al., NeurIPS 2019)

BLiMP: The Benchmark of Linguistic Minimal Pairs for English (Warstadt et al., TACL 2020)

Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora (Warstadt et al., CoNLL-BabyLM 2023)

🌏 Elements of World Knowledge (EWoK): A cognition-inspired framework for evaluating basic world knowledge in language models (Ivanova et al., 2024)

Cloze probability, predictability ratings, and computational estimates for 205 English sentences, aligned with existing EEG and reading time data (de Varda et al., BRM 2024)

Entity Tracking in Language Models (Kim & Schuster, ACL 2023)

Derivational Morphology Reveals Analogical Generalization in Large Language Models (Hofmann et al., 2024)

Automatically Constructing a Corpus of Sentential Paraphrases (Dolan & Brockett, IJCNLP 2005)

A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference (Williams et al., NAACL 2018)

The Winograd Schema Challenge (Levesque et al., PKRR 2012)

The PASCAL Recognising Textual Entailment Challenge (Dagan et al., Springer 2006)

The Second PASCAL Recognising Textual Entailment Challenge (Bar et al., 2006)

The Third PASCAL Recognizing Textual Entailment Challenge (Giampiccolo et al., 2007)

The Fifth PASCAL Recognizing Textual Entailment Challenge (Bentivogli et al., TAC 2009)

BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions (Clark et al., NAACL 2019)

Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences (Khashabi et al., NAACL 2018)