  Over the past decade, analogies, in the form of word-level analogies, have
played a significant role as an intrinsic measure of evaluating the quality of
word embedding methods such as word2vec. Modern large language models (LLMs),
however, are primarily evaluated on extrinsic measures based on benchmarks such
as GLUE and SuperGLUE, and there are only a few investigations on whether LLMs
can draw analogies between long texts. In this paper, we present ANALOGICAL, a
new benchmark to intrinsically evaluate LLMs across a taxonomy of analogies of
long text with six levels of complexity -- (i) word, (ii) word vs. sentence,
(iii) syntactic, (iv) negation, (v) entailment, and (vi) metaphor. Using
thirteen datasets and three different distance measures, we evaluate the
abilities of eight LLMs in identifying analogical pairs in the semantic vector
space. Our evaluation finds that it is increasingly challenging for LLMs to
identify analogies when going up the analogy taxonomy.

Computation and Language;Artificial Intelligence