Iterative evaluation of LLMs during training is essential to ensure expected capability development, but can be time- and compute-intensive. While NLU tasks, where the model selects from fixed answer choices, are cheap to evaluate, essential capabilities like reasoning and code generation rely on the more time-consuming NLG (token-by-token generation) format. In this work, our aim is to decrease the computational burden of NLG benchmarks in order to enable monitoring crucial LLM capabilities during model training. We reformulate generative tasks into computationally cheaper NLU alternatives. We test the performance correlation between the original and reformulated tasks using 8 LMs of various sizes and 4 capabilities: mathematical reasoning, code generation, factual knowledge and reading comprehension. Our results show a strong correlation between task formats, supporting capability assessment via cheaper alternatives and achieving over 35x average reduction in evaluation time. Our project is available at: https://github.com/Fraunhofer-IIS/EvalShortcut

Recent work shows that post-training datasets for LLMs can be substantially downsampled without noticeably deteriorating performance. However, data selection often incurs high computational costs or is limited to narrow domains. In this paper, we demonstrate that data selection can be both—efficient and universal—by using a multi-step pipeline in which we efficiently bin data points into groups, estimate quality using specialized models, and score difficulty with a robust, lightweight method. Task-based categorization allows us to control the composition of our final data—crucial for finetuning multi-purpose models. To guarantee diversity, we improve upon previous work using embedding models and a clustering algorithm. This integrated strategy enables high-performance fine-tuning with minimal overhead.

Large Transformer-based natural language understanding models have achieved state-of-the-art performance in dialogue systems. However, scarce labeled data for training, the large model size, and low inference speed hinder their deployment in low-resource scenarios. Few-shot learning and knowledge distillation techniques have been introduced to reduce the need for labeled data and computational resources, respectively. However, these techniques are incompatible because few-shot learning trains models using few data, whereas, knowledge distillation requires sufficient data to train smaller, yet competitive models that run on limited computational resources. In this paper, we address the problem of distilling generalizable small models under the few-shot setting for the intent classification task. Considering in-domain and cross-domain few-shot learning scenarios, we introduce an approach for distilling small models that generalize to new intent classes and domains using only a handful of labeled examples. We conduct experiments on public intent classification benchmarks, and observe a slight performance gap between small models and large Transformer-based models. Overall, our results in both few-shot scenarios confirm the generalization ability of the small distilled models while having lower computational costs.