Papers

arXiv 링크를 붙여 넣거나 논문 PDF를 올리면 읽기 에이전트가 읽기 좋은 페이지로 펼칩니다. 어려운 대목마다 여백 노트를, 모든 그림에 해설을, 저자가 얼버무린 부분에는 반박 리뷰를 답니다.

논문을 제출하려면 로그인이 필요합니다. 읽는 것은 누구나 할 수 있습니다.

읽을 준비가 된 논문

Restoring Voluntary Control of Locomotion after Paralyzing Spinal Cord InjuryHalf of human spinal cord injuries lead to chronic paralysis. Here, we introduce an electrochemical neuroprosthesis and a robotic postural interface designed to encourage supraspinally mediated movements in rats with paralyzing lesions. Despite the interruption of direct supraspinal pathways, the cortex regained the capacity to transform contextual information into task-specific commands to execute refined locomotion. This recovery relied on the extensive remodeling of cortical projections, including the formation of brainstem and intraspinal relays that restored qualitative control over electrochemically enabled lumbosacral circuitries. Automated treadmill-restricted training, which did not engage cortical neurons, failed to promote translesional plasticity and recovery. By encouraging active participation under functional states, our training paradigm triggered a cortex-dependent recovery that may improve function after similar injuries in humans.Rubia van den Brand · Janine Heutschi · Quentin Barraud · Jack DiGiovanna · Kay Bartholdi · Michèle Huerlimann · Lucia Friedli · Isabel Vollenweider · Eduardo Martin Moraud · Simone Duis · Nadia Dominici · Silvestro Micera · Pavel Musienko · Grégoire Courtine
Attention Is All You NeedThe dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.Ashish Vaswani · Noam Shazeer · Niki Parmar · Jakob Uszkoreit · Llion Jones · Aidan N. Gomez · Łukasz Kaiser · Illia Polosukhin
Mixture-of-Agents Enhances Large Language Model CapabilitiesRecent advances in large language models (LLMs) demonstrate substantial capabilities in natural language understanding and generation tasks. With the growing number of LLMs, how to harness the collective expertise of multiple LLMs is an exciting open direction. Toward this goal, we propose a new approach that leverages the collective strengths of multiple LLMs through a Mixture-of-Agents (MoA) methodology. In our approach, we construct a layered MoA architecture wherein each layer comprises multiple LLM agents. Each agent takes all the outputs from agents in the previous layer as auxiliary information in generating its response. MoA models achieves state-of-art performance on AlpacaEval 2.0, MT-Bench and FLASK, surpassing GPT-4 Omni. For example, our MoA using only open-source LLMs is the leader of AlpacaEval 2.0 by a substantial gap, achieving a score of 65.1% compared to 57.5% by GPT-4 Omni.111Our code can be found in: <https://github.com/togethercomputer/moa>.Junlin Wang · Jue Wang · Ben Athiwaratkun · Ce Zhang · James Zou