BabyLM Challenge (2023)

Venues:
CoNLL | BabyLM | WS

Volumes

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pdf (full)
bib (full) Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning

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Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning
Alex Warstadt | Aaron Mueller | Leshem Choshen | Ethan Wilcox | Chengxu Zhuang | Juan Ciro | Rafael Mosquera | Bhargavi Paranjabe | Adina Williams | Tal Linzen | Ryan Cotterell
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Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora
Alex Warstadt | Aaron Mueller | Leshem Choshen | Ethan Wilcox | Chengxu Zhuang | Juan Ciro | Rafael Mosquera | Bhargavi Paranjabe | Adina Williams | Tal Linzen | Ryan Cotterell
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GPT-wee: How Small Can a Small Language Model Really Get?
Bastian Bunzeck | Sina Zarrieß
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Tiny Language Models Enriched with Multimodal Knowledge from Multiplex Networks
Clayton Fields | Osama Natouf | Andrew McMains | Catherine Henry | Casey Kennington
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Mini Minds: Exploring Bebeshka and Zlata Baby Models
Irina Proskurina | Guillaume Metzler | Julien Velcin
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Grammar induction pretraining for language modeling in low resource contexts
Xuanda Chen | Eva Portelance
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ChapGTP, ILLC’s Attempt at Raising a BabyLM: Improving Data Efficiency by Automatic Task Formation
Jaap Jumelet | Michael Hanna | Marianne de Heer Kloots | Anna Langedijk | Charlotte Pouw | Oskar van der Wal
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Penn & BGU BabyBERTa+ for Strict-Small BabyLM Challenge
Yahan Yang | Elior Sulem | Insup Lee | Dan Roth
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Too Much Information: Keeping Training Simple for BabyLMs
Lukas Edman | Lisa Bylinina
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Can training neural language models on a curriculum with developmentally plausible data improve alignment with human reading behavior?
Aryaman Chobey | Oliver Smith | Anzi Wang | Grusha Prasad
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CLIMB – Curriculum Learning for Infant-inspired Model Building
Richard Diehl Martinez | Zébulon Goriely | Hope McGovern | Christopher Davis | Andrew Caines | Paula Buttery | Lisa Beinborn
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Acquiring Linguistic Knowledge from Multimodal Input
Theodor Amariucai | Alexander Scott Warstadt
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Large GPT-like Models are Bad Babies: A Closer Look at the Relationship between Linguistic Competence and Psycholinguistic Measures
Julius Steuer | Marius Mosbach | Dietrich Klakow
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Baby’s CoThought: Leveraging Large Language Models for Enhanced Reasoning in Compact Models
Zheyu Zhang | Han Yang | Bolei Ma | David Rügamer | Ercong Nie
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ToddlerBERTa: Exploiting BabyBERTa for Grammar Learning and Language Understanding
Ömer Veysel Çağatan
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CogMemLM: Human-Like Memory Mechanisms Improve Performance and Cognitive Plausibility of LLMs
Lukas Thoma | Ivonne Weyers | Erion Çano | Stefan Schweter | Jutta L Mueller | Benjamin Roth
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BabyStories: Can Reinforcement Learning Teach Baby Language Models to Write Better Stories?
Xingmeng Zhao | Tongnian Wang | Sheri Osborn | Anthony Rios
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Byte-ranked Curriculum Learning for BabyLM Strict-small Shared Task 2023
Justin DeBenedetto
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McGill BabyLM Shared Task Submission: The Effects of Data Formatting and Structural Biases
Ziling Cheng | Rahul Aralikatte | Ian Porada | Cesare Spinoso-Di Piano | Jackie CK Cheung
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Mean BERTs make erratic language teachers: the effectiveness of latent bootstrapping in low-resource settings
David Samuel
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Not all layers are equally as important: Every Layer Counts BERT
Lucas Georges Gabriel Charpentier | David Samuel
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WhisBERT: Multimodal Text-Audio Language Modeling on 100M Words
Lukas Wolf | Klemen Kotar | Greta Tuckute | Eghbal Hosseini | Tamar I. Regev | Ethan Gotlieb Wilcox | Alex Warstadt
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A surprisal oracle for active curriculum language modeling
Xudong Hong | Sharid Loáiciga | Asad Sayeed
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Mmi01 at The BabyLM Challenge: Linguistically Motivated Curriculum Learning for Pretraining in Low-Resource Settings
Maggie Mi
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Baby Llama: knowledge distillation from an ensemble of teachers trained on a small dataset with no performance penalty
Inar Timiryasov | Jean-Loup Tastet
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BabyLM Challenge: Curriculum learning based on sentence complexity approximating language acquisition
Miyu Oba | Akari Haga | Akiyo Fukatsu | Yohei Oseki
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Better Together: Jointly Using Masked Latent Semantic Modeling and Masked Language Modeling for Sample Efficient Pre-training
Gábor Berend
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Lil-Bevo: Explorations of Strategies for Training Language Models in More Humanlike Ways
Venkata S Govindarajan | Juan Diego Rodriguez | Kaj Bostrom | Kyle Mahowald
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Towards more Human-like Language Models based on Contextualizer Pretraining Strategy
Chenghao Xiao | G Thomas Hudson | Noura Al Moubayed
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Increasing The Performance of Cognitively Inspired Data-Efficient Language Models via Implicit Structure Building
Omar Momen | David Arps | Laura Kallmeyer
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Pre-training LLMs using human-like development data corpus
Khushi Bhardwaj | Raj Sanjay Shah | Sashank Varma
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On the effect of curriculum learning with developmental data for grammar acquisition
Mattia Opper | J. Morrison | N. Siddharth
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Optimizing GPT-2 Pretraining on BabyLM Corpus with Difficulty-based Sentence Reordering
Nasim Borazjanizadeh