Large language models (LLMs) have been widely deployed and have achieved remarkable success in downstream tasks. However, their high latency continues to pose challenges for real-time applications that require fast inference, and the need to train and deploy distinct models for different hardware constraints increases both financial and computational costs. To address this, we propose Nested Matrix Learning (NML), a method that trains a single, flexible model capable of generating multiple high-performing student models of varying sizes. This is achieved by simultaneously optimizing a pre-trained teacher model and its nested sub-models in a single training process, without sacrificing the teacher’s performance. NML provides a flexible and scalable solution, allowing models to adapt to different computational budgets. Our extensive experiments show that student models produced by NML, which can be up to 10x smaller than the full-size model, can be directly deployed for efficient inference or serve as superior initialization points for further fine-tuning in downstream tasks. By preserving the performance of the teacher model while delivering compact and efficient student models of various sizes, NML enhances the usability and adaptability of LLMs in real-world scenarios.

Parallel deep learning architectures like fine-tuned BERT and MT-DNN, have quickly become the state of the art, bypassing previous deep and shallow learning methods by a large margin. More recently, pre-trained models from large related datasets have been able to perform well on many downstream tasks by just fine-tuning on domain-specific datasets (similar to transfer learning). However, using powerful models on non-trivial tasks, such as ranking and large document classification, still remains a challenge due to input size limitations of parallel architecture and extremely small datasets (insufficient for fine-tuning). In this work, we introduce an end-to-end system, trained in a multi-task setting, to filter and re-rank answers in the medical domain. We use task-specific pre-trained models as deep feature extractors. Our model achieves the highest Spearman’s Rho and Mean Reciprocal Rank of 0.338 and 0.9622 respectively, on the ACL-BioNLP workshop MediQA Question Answering shared-task.

The field of question answering (QA) has seen rapid growth in new tasks and modeling approaches in recent years. Large scale datasets and focus on challenging linguistic phenomena have driven development in neural models, some of which have achieved parity with human performance in limited cases. However, an examination of state-of-the-art model output reveals that a gap remains in reasoning ability compared to a human, and performance tends to degrade when models are exposed to less-constrained tasks. We are interested in more clearly defining the strengths and limitations of leading models across diverse QA challenges, intending to help future researchers with identifying pathways to generalizable performance. We conduct extensive qualitative and quantitative analyses on the results of four models across four datasets and relate common errors to model capabilities. We also illustrate limitations in the datasets we examine and discuss a way forward for achieving generalizable models and datasets that broadly test QA capabilities.