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Bishwaranjan Bhattacharjee, Aashka Trivedi, Masayasu Muraoka +31 authors
INDUS, a suite of domain-specific LLMs trained on curated scientific corpora, surpasses general-purpose and existing domain-specific models on specialized NLP tasks and new scientific benchmarks.
Chunjing Gan, Dan Yang, Binbin Hu +9 authors
MetRag enhances retrieval-augmented generation by integrating similarity and utility-oriented thoughts, employing an LLM as a task adaptive summarizer, and leveraging multi-layered thoughts for knowledge-intensive tasks.
Viraat Aryabumi, John Dang, Dwarak Talupuru +14 authors
Aya 23 is a powerful multilingual language model for 23 languages, outperforming previous models on a wide range of tasks.
Xueyan Niu, Bo Bai, Lei Deng +1 authors
A theoretical framework using Hopfield networks explains memorization and performance dynamics in Transformer-based language models, showing bounds on achievable cross-entropy loss.
Peitian Zhang, Ninglu Shao, Zheng Liu +4 authors
Llama-3-8B-Instruct's context length is extended from 8K to 80K using QLoRA fine-tuning with minimal additional training samples, demonstrating significant potential for further context extension with increased computational resources.
William Brandon, Mayank Mishra, Aniruddha Nrusimha +2 authors
Cross-Layer Attention modifies transformer-based autoregressive large language models to reduce KV cache size while maintaining accuracy, enabling longer sequence lengths and larger batch sizes during inference.
Hugh Zhang, Jeff Da, Dean Lee +12 authors
Evaluation of large language models on a new math benchmark, GSM1k, reveals that many models exhibit signs of overfitting to the existing GSM8k benchmark, leading to performance drops on GSM1k.
Yixing Jiang, Jeremy Irvin, Ji Hun Wang +3 authors
Multimodal foundation models exhibit significant performance improvements with many-shot in-context learning compared to few-shot, with Gemini 1.5 Pro showing higher data efficiency and better batch processing capabilities across various domains.
Oleksiy Ostapenko, Zhan Su, Edoardo Maria Ponti +5 authors
A novel approach using model-based clustering and zero-shot routing improves the generalization of adapters in large language models for new tasks.
Tianhe Ren, Qing Jiang, Shilong Liu +13 authors
Grounding DINO 1.5, comprising Pro and Edge models, enhances open-set object detection through advanced architectures and large datasets, achieving high performance and efficiency.
Eloi Alonso, Adam Jelley, Vincent Micheli +4 authors
DIAMOND, a reinforcement learning agent using a diffusion world model, achieves top performance on Atari 100k by capturing visual details effectively.
Sheng-Chieh Lin, Luyu Gao, Barlas Oguz +4 authors
Proposes factuality-aware alignment methods for pre-trained language models to enhance factual accuracy during supervised fine-tuning and reinforcement learning, reducing hallucination.
Gerald Shen, Zhilin Wang, Olivier Delalleau +10 authors
NeMo-Aligner is a toolkit for aligning large language models with human values using scalable techniques like RLHF, DPO, SteerLM, and SPIN, optimized for use with hundreds of GPUs and parameter-efficient fine-tuning.
Navve Wasserman, Noam Rotstein, Roy Ganz +1 authors
A diffusion model is trained to add objects to images based on text descriptions using a large-scale dataset of images with removed objects, outperforming existing methods.
Octo Model Team, Dibya Ghosh, Homer Walke +15 authors
Octo, a large transformer-based policy trained on extensive robotic datasets, demonstrates versatility and efficient fine-tuning for diverse robotic platforms and tasks.
Zhenwei Shao, Zhou Yu, Jun Yu +5 authors
A systematic study of lightweight large multimodal models (LMMs) led to the development of the Imp family, which achieves superior performance compared to larger models and is capable of high-speed inference on mobile devices.
Yue Wu, Zhiqing Sun, Huizhuo Yuan +3 authors
A self-play method called SPPO for language model alignment achieves state-of-the-art performance by approximating Nash equilibrium policy in a constant-sum game setting, outperforming other approaches with limited data.
Wenyu Du, Tongxu Luo, Zihan Qiu +5 authors
A depthwise stacking operator called $G_{\text{stack}}$ accelerates LLM pre-training, improving performance with less computational expense and providing guidelines for effective use.
Wenxun Dai, Ling-Hao Chen, Jingbo Wang +3 authors
MotionLCM generates real-time controllable human motions using text and control signals by extending latent diffusion models with a Motion ControlNet.
Aaron Defazio, Xingyu, Yang +4 authors
A new schedule-free method achieves state-of-the-art performance in optimization without requiring a stopping time or additional hyper-parameters, by unifying scheduling and iterate averaging.
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