Multiple-choice benchmarks are cheap to grade and are running out of room, and the standard remedy, writing harder items, is slow and repeated for every benchmark. A saturated benchmark still holds a harder task. Each question's wrong options are written for that question alone, so a model can score by eliminating a fe...
M. Eltahir, Abobaker Ahmed, Nawaf Barebood et al.· 0 citations
Answer-Convergence Stopping (ACS) is introduced, a training-free stopping rule that measures rather than asks, and reveals that by properly utilizing the output signals of frozen models, it can achieve favorable behaviors like adaptive stopping without the need for additional training.
Muath Alyobi, M. Eltahir, Almoayyad Abuljdail et al.· 0 citations
Only on MathNet-Retrieve could an inversion, benchmark score up and real retention down, to one edit of a training file be pined, and three trained models are released.
A. Habibullah, M. Alshiekh, Yazan Alshoibi et al.· 0 citations
The preamble worsens ten models, moving three out of the band into collapse and one into refusal, yet improves seven whose neutral prompts over-mark: the vulnerability replicates, but its direction is exam-specific.
A. Habibullah, Yazan Alshoibi, M. Alshiekh et al.· 0 citations
Generative Late-Interaction Embeddings (GLIE): k<<N vectors per page learned from the normalized centroids to serve as both a lightweight index and a basis for regenerating the page's full embedding set, with the decoder as its main design surface.
M. Eltahir, Talal Aloushan, Rose Khairoalsendi et al.· 0 citations
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