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20182026
most citedFindings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora

72 citations · 289 across the 69 of their papers we have counts for

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Showing 2026Show all

7 papers · 1 filter

cs.CL2026

Why Pretraining Fails to Share Cross-Lingual Knowledge

Adam Gaber, Uriel Dolev, Elisabeth Fittschen +3

Large Language Models (LLMs) have made remarkable progress in the processing and modeling of many languages. Yet, unlike human multilinguals, they exhibit surprisingly limited cros…

cs.CL2026

Last Translation Benchmark

Vilém Zouhar, Niyati Bafna, Mukund Choudhary +263

For scientific progress, we need benchmarks that test the limits of state-of-the-art models, and evaluation methods that inform us about failure cases. As models get stronger, stan…

cs.CL2026

User Feedback Provides a Unique Signal that LLMs Can not Detect

Shachar Don-Yehiya, Leshem Choshen, Omri Abend

Harnessing naturally occurring feedback from user interactions offers a promising learning signal for Large Language Models (LLMs). However, recent studies suggest this feedback is…

cs.CL2026

Cross-Lingual Exploration for Parametric Knowledge

Elisha Diskind, Itamar Trainin, Uri Shaham +3

Parametric knowledge in Large Language Models is not equally accessible across languages. As a result, standard inference techniques often struggle to surface localized facts, lead…

cs.CL2026

Mediocrity is the key for LLM as a Judge Anchor Selection

Shachar Don-Yehiya, Asaf Yehudai, Leshem Choshen +1

The ``LLM-as-a-judge'' paradigm has become a standard method for evaluating open-ended generation. To address the quadratic scalability costs of pairwise comparisons, popular bench…

cs.CL2026

BabyLM Turns 4 and Goes Multilingual: Call for Papers for the 2026 BabyLM Workshop

Leshem Choshen, Ryan Cotterell, Mustafa Omer Gul +7

The goal of the BabyLM is to stimulate new research connections between cognitive modeling and language model pretraining. We invite contributions in this vein to the BabyLM Worksh…

cs.AI2026★ 2 cited

General Agent Evaluation

Elron Bandel, Asaf Yehudai, Lilach Eden +12

General-purpose agents perform tasks in unfamiliar environments without domain-specific manual customization. Yet no study has systematically measured how agent architecture shapes…