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Predicting the Performance of Foundation Models via Agreement-on-the-Line
Second, FMs are leveraged for many different tasks (e.g., classification,
Transcoders Find Interpretable LLM Feature Circuits
A key goal in mechanistic interpretability is circuit analysis: finding sparse sub-graphs of models corresponding to specific behaviors or capabilities.
QA: A Question-Answering Dataset for Benchmarking LLMs on Unseen Reference Content João Monteiro,3, Pierre-André Noël
Consequently, evaluating models on test splits that might have leaked into the training set is prone to misleading conclusions.
Uncertainty of Thoughts: Uncertainty-Aware Planning Enhances Information Seeking in LLMs
In the face of uncertainty, the ability to seek information is of fundamental importance.
Vision Model Pre-training on Interleaved Image-Text Data via Latent Compression Learning Chenyu Y ang
Raw pixels are unstructured and often contain unnecessary and unpredictable details.
Debiased and Denoised Entity Recognition from Distant Supervision Haobo Wang
A few past works approach this problem by adopting a self-training framework with a sample-selection mechanism.
Getting ViT in Shape: Scaling Laws for Compute-Optimal Model Design
However, the simple power-law relation becomes more complicated when compute is considered.