The rapid expansion of Large Language Models (LLMs) and natural language processing datasets has made exhaustive benchmark evaluations computationally prohibitive. Inspired by high-stakes competitions like the International Mathematical Olympiad-where a few well-chosen problems suffice to differentiate top performers—we present SubLIME, which reduces evaluation costs by 80% to 99% while preserving ranking fidelity. It trains a Rank Correlation Prediction (RCP) model that combines limited performance data from only 5-20 anchor LLMs with dataset intrinsic metrics - Difficulty, Quality, and Distributional Dispersion-to predict how closely a candidate subset reflects full-benchmark rankings. Guided by these predictions, SubLIME selects a “winning” subset (1-20% of full set data) for evaluating new LLMs, preserving global rankings significant better than other data-efficient methods across ten diverse benchmarks.

Recent work on automatic sequential metaphor detection has involved recurrent neural networks initialized with different pre-trained word embeddings and which are sometimes combined with hand engineered features. To capture lexical and orthographic information automatically, in this paper we propose to add character based word representation. Also, to contrast the difference between literal and contextual meaning, we utilize a similarity network. We explore these components via two different architectures - a BiLSTM model and a Transformer Encoder model similar to BERT to perform metaphor identification. We participate in the Second Shared Task on Metaphor Detection on both the VUA and TOFEL datasets with the above models. The experimental results demonstrate the effectiveness of our method as it outperforms all the systems which participated in the previous shared task.