Hugging Face Trending Papers
Improving JEV Entailment Reranking with Span Readouts and Selective Adaptation
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arXiv Computation and Language
Oct 27
Improving JEV Entailment Reranking with Span Readouts and Selective Adaptation
arXiv:2610.35654v1 Announce Type: new Abstract: JEV decision models pair a language model backbone with a lightweight scoring head to select among natural-language options. Direct selection compares...
Hugging Face Trending Papers
Jul 22
Statistical Inference for Rank Allocation in Low-Rank Adaptation
Low-rank adaptation (LoRA) has become a widely used parameter-efficient fine-tuning method for large language models. Since different modules and layers may contribute unequally to downstream adaptation, allocating rank resources under a fixed parameter budget is an important problem for balancing efficiency, expressiveness, and generalization.
arXiv Machine Learning
Jul 23
Statistical Inference for Rank Allocation in Low-Rank Adaptation
arXiv:2607. 20205v1 Announce Type: cross Abstract: Low-rank adaptation (LoRA) has become a widely used parameter-efficient fine-tuning method for large language models.
arXiv Machine Learning
Jun 2
RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models
arXiv:2505. 18877v4 Announce Type: replace Abstract: Low-Rank Adaptation (LoRA) lowers the computational and memory overhead of fine-tuning large models by updating a low-dimensional subspace of the pre-trained weight matrix.
arXiv Computation and Language
Sep 23
ChainDoRA: Tensor-Train Factorized Weight-Decomposed Low-Rank Adaptation for Parameter-Efficient LLM Fine-Tuning
ChainDoRA is a new parameter‑efficient fine‑tuning framework for large language models that replaces the dense low‑rank factorization of LoRA with a connected Tensor‑Train (TT) chain. By separating weight magnitude and direction and using a TT rank to control representation capacity, ChainDoRA achieves higher average accuracy on seven commonsense reasoning benchmarks while dramatically reducing trainable parameters—down to 5.35 M versus 56 M for LoRA and DoRA. Ablation studies show that the TT parameterization offers controllable trade‑offs between parameter cost and accuracy.
Hugging Face Trending Papers
6d ago
Glance: The Generator Is Its Own Reranker
Glance is a retrieval‑augmented generation approach that eliminates the need for separate reranker models by adding a lightweight decision adapter and pointer head to a single 9B backbone. It scores candidate passages directly from a shared prefill, requiring only about 0.1 GiB of extra memory per decision skill. In fine‑tuning on MS MARCO, Glance outperforms the bge‑reranker‑v2‑m3 on SciFact and NFCorpus, and improves answer F1 on HotpotQA by 0.045, while scaling to 200 candidates it surpasses cross‑encoder reranking by 0.058 nDCG@10.