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Found 10 papers, 9 papers with code
2 OLMo 2 Furious
Our modified model architecture and training recipe achieve both better training stability and improved per-token efficiency.
Tulu 3: Pushing Frontiers in Open Language Model Post-Training
Language model post-training is applied to refine behaviors and unlock new skills across a wide range of recent language models, but open recipes for applying these techniques lag behind proprietary ones.
Merge to Learn: Efficiently Adding Skills to Language Models with Model Merging
Adapting general-purpose language models to new skills is currently an expensive process that must be repeated as new instruction datasets targeting new skills are created, or can cause the models to forget older skills.
OLMoE: Open Mixture-of-Experts Language Models
We introduce OLMoE, a fully open, state-of-the-art language model leveraging sparse Mixture-of-Experts (MoE).
SciRIFF: A Resource to Enhance Language Model Instruction-Following over Scientific Literature
We present SciRIFF (Scientific Resource for Instruction-Following and Finetuning), a dataset of 137K instruction-following demonstrations for 54 tasks covering five essential scientific literature understanding capabilities: information extraction, summarization, question answering, claim verification, and classification.
RewardBench: Evaluating Reward Models for Language Modeling
To enhance scientific understanding of reward models, we present RewardBench, a benchmark dataset and code-base for evaluation.
OLMo: Accelerating the Science of Language Models
Given the importance of these details in scientifically studying these models, including their biases and potential risks, we believe it is essential for the research community to have access to powerful, truly open LMs.
Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research
As a result, it is challenging to conduct and advance scientific research on language modeling, such as understanding how training data impacts model capabilities and limitations.
Bidimensional Leaderboards: Generate and Evaluate Language Hand in Hand
We therefore propose a generalization of leaderboards, bidimensional leaderboards (Billboards), that simultaneously tracks progress in language generation models and metrics for their evaluation.
Transparent Human Evaluation for Image Captioning
We establish THumB, a rubric-based human evaluation protocol for image captioning models.
