VRSD is defined by maximizing query-to-sum similarity, proven NP-complete, with a parameter-free heuristic outperforming MMR and DPP baselines.
Think you have solved direct-answer question answering? try arc-da, the direct-answer AI2 reasoning challenge.CoRR, abs/2102.03315
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
verdicts
UNVERDICTED 6representative citing papers
RUQuant uses block-wise composite orthogonal matrices from Householder reflections and Givens rotations plus a fine-tuned global reflection to achieve 99.8% full-precision accuracy at W6A6 and 97% at W4A4 for 13B LLMs in about one minute.
DITS replaces Q-value guidance in MCTS with influence scores for synthetic data synthesis in multi-agent LLM training, claiming better efficiency and performance on eight datasets.
CRAG improves RAG robustness via a retrieval quality evaluator that triggers web augmentation and a decompose-recompose filter to focus on relevant information, yielding better results on short- and long-form generation tasks.
SEPTQ simplifies LLM post-training quantization to two steps via static global importance scoring and mask-guided column-wise weight updates, claiming superior results over baselines in low-bit settings.
LLM accuracy on reasoning tasks differs significantly by question type, with step-by-step reasoning accuracy often uncorrelated to final answer selection.
citing papers explorer
-
Vector Retrieval with Similarity and Diversity: How Hard Is It?
VRSD is defined by maximizing query-to-sum similarity, proven NP-complete, with a parameter-free heuristic outperforming MMR and DPP baselines.
-
RUQuant: Towards Refining Uniform Quantization for Large Language Models
RUQuant uses block-wise composite orthogonal matrices from Householder reflections and Givens rotations plus a fine-tuned global reflection to achieve 99.8% full-precision accuracy at W6A6 and 97% at W4A4 for 13B LLMs in about one minute.
-
Efficient Multi-Agent System Training with Data Influence-Oriented Tree Search
DITS replaces Q-value guidance in MCTS with influence scores for synthetic data synthesis in multi-agent LLM training, claiming better efficiency and performance on eight datasets.
-
Corrective Retrieval Augmented Generation
CRAG improves RAG robustness via a retrieval quality evaluator that triggers web augmentation and a decompose-recompose filter to focus on relevant information, yielding better results on short- and long-form generation tasks.
-
SEPTQ: A Simple and Effective Post-Training Quantization Paradigm for Large Language Models
SEPTQ simplifies LLM post-training quantization to two steps via static global importance scoring and mask-guided column-wise weight updates, claiming superior results over baselines in low-bit settings.
-
Is Large Language Model Performance on Reasoning Tasks Impacted by Different Ways Questions Are Asked?
LLM accuracy on reasoning tasks differs significantly by question type, with step-by-step reasoning accuracy often uncorrelated to final answer selection.