NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models | Paper Citation Record

Observation 95e80969-1d94-45df-8bf0-4334f80bc952 · outbound

This paper cites The Llama 3 Herd of Models.

NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models The Llama 3 Herd of Models

Reference 1

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This paper cites Qwen2.5 Technical Report.

NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models Qwen2.5 Technical Report

Reference 2

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This paper cites Falcon2-11B Technical Report.

NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models Falcon2-11B Technical Report

Reference 3

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This paper cites A Survey of Large Language Models.

NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models A Survey of Large Language Models

Reference 4

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This paper cites HellaSwag: Can a Machine Really Finish Your Sentence?.

NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 5

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This paper cites Winogrande: An adversarial winograd schema challenge at scale.

NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models Winogrande: An adversarial winograd schema challenge at scale

Reference 6

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This paper cites Measuring massive multitask language understanding.

NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models Measuring massive multitask language understanding

Reference 7

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This paper cites Mmlu-pro: A more robust and challenging multi-task language understanding benchmark.

NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models Mmlu-pro: A more robust and challenging multi-task language understanding benchmark

Reference 8

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This paper cites Gpqa: A graduate-level google-proof q&a benchmark.

NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models Gpqa: A graduate-level google-proof q&a benchmark

Reference 9

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Observation 344ce59a-6fd8-40d4-b415-f2f574edc337 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models Training Verifiers to Solve Math Word Problems

Reference 10

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This paper cites Measuring Mathematical Problem Solving With the MATH Dataset.

NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models Measuring Mathematical Problem Solving With the MATH Dataset

Reference 11

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Observation ffb23c3c-4554-42f2-9141-b0ec61f7c258 · outbound

This paper cites LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code.

NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code

Reference 12

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This paper cites What is the Role of Small Models in the LLM Era: A Survey.

NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models What is the Role of Small Models in the LLM Era: A Survey

Reference 13

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Observation 52abb96c-b5eb-434a-a4a2-e9fb7259ebac · outbound

This paper cites Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer.

NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer

Reference 14

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This paper cites OLMoE: Open Mixture-of-Experts Language Models.

NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models OLMoE: Open Mixture-of-Experts Language Models

Reference 15

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This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 16

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This paper cites Liu, and Matt Gardner.

NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models Liu, and Matt Gardner

Reference 17

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This paper cites Toward an Evaluation Science for Generative AI Systems.

NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models Toward an Evaluation Science for Generative AI Systems

Reference 18

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This paper cites Efficient Large Language Models: A Survey.

NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models Efficient Large Language Models: A Survey

Reference 19

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Observation 3870a5ef-4ab3-4d4e-a946-e1d41744f0c6 · outbound

This paper cites Scaling Laws for Neural Language Models.

NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models Scaling Laws for Neural Language Models

Reference 20

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This paper cites Training Compute-Optimal Large Language Models.

NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models Training Compute-Optimal Large Language Models

Reference 21

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This paper cites A survey on large language models: Applications, challenges, limitations, and practical usage.

NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models A survey on large language models: Applications, challenges, limitations, and practical usage

Reference 22

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This paper cites Llm merging: Building llms efficiently through merging.

NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models Llm merging: Building llms efficiently through merging

Reference 23

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This paper cites Edge-llms: Edge-device large language model competition.

NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models Edge-llms: Edge-device large language model competition

Reference 24

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This paper cites https://llm-efficiency- challenge.github.io/, 2024.

NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models https://llm-efficiency- challenge.github.io/, 2024

Reference 25

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NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models Stages and individual differences in cognitive development

Reference 26

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NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models The new taxonomy of educational objectives

Reference 27

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NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models Taxonomy ofeducational objectives the clas- sification ofeducational goals

Reference 28

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This paper cites The fineweb datasets: Decanting the web for the finest text data at scale.

NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models The fineweb datasets: Decanting the web for the finest text data at scale

Reference 29

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NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models Fineweb-edu: the finest collection of educational content, 2024

Reference 30

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NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models The stack: 3 tb of permissively licensed source code

Reference 31

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NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models Starcoder 2 and the stack v2: The next generation, 2024

Reference 32

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NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models Infimm-webmath-40b: Advancing multimodal pre-training for enhanced mathematical reasoning, 2024

Reference 33

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NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models Unresolved cited work

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NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models Physics of Language Models: Part 3.1, Knowledge Storage and Extraction

Reference 35

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NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models Unresolved cited work

Reference 36

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This paper cites Finetasks: Finding signal in a haystack of 200+ multilingual tasks.

NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models Finetasks: Finding signal in a haystack of 200+ multilingual tasks

Reference 37

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NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models Codabench: Flexible, easy-to-use, and reproducible meta-benchmark platform

Reference 38

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This paper cites The LAMBADA dataset: Word prediction requiring a broad discourse context.

NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models The LAMBADA dataset: Word prediction requiring a broad discourse context

Reference 39

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NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models Piqa: Reasoning about physical commonsense in natural language

Reference 40

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NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models Instruction-following evaluation for large language models, 2023

Reference 41

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NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models RACE: Large-scale ReAding comprehension dataset from examinations

Reference 42

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NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models Semantic parsing on Freebase from question-answer pairs

Reference 43

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This paper cites Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them.

NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 44

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NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models DROP: A reading comprehension benchmark requiring discrete reasoning over paragraphs

Reference 45

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NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models Boolq: Exploring the surprising difficulty of natural yes/no questions

Reference 46

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NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models Adversarial nli: A new benchmark for natural language understanding

Reference 47

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This paper cites TruthfulQA: Measuring How Models Mimic Human Falsehoods.

NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models TruthfulQA: Measuring How Models Mimic Human Falsehoods

Reference 48

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NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models Mask R-CNN

Reference 49

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This paper cites 26 Figure 8: Validation loss across different datasets for two model variants: Scientific-DataMix (blue) and webOnly-Data (red).

NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models 26 Figure 8: Validation loss across different datasets for two model variants: Scientific-DataMix (blue) and webOnly-Data (red)

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:24.732017Z

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This paper cites Lower loss on one dataset does not necessarily translate to higher capability on corresponding benchmarks.

NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models Lower loss on one dataset does not necessarily translate to higher capability on corresponding benchmarks

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:24.713641Z

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NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models Unresolved cited work

Reference 53

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NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models Unresolved cited work

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NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models Unresolved cited work

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