  While Large Language Models (LLMs) become ever more dominant, classic
pre-trained word embeddings sustain their relevance through computational
efficiency and nuanced linguistic interpretation. Drawing from recent studies
demonstrating that the convergence of GloVe and word2vec optimizations all tend
towards log-co-occurrence matrix variants, we construct a novel word
representation system called Bit-cipher that eliminates the need of
backpropagation while leveraging contextual information and hyper-efficient
dimensionality reduction techniques based on unigram frequency, providing
strong interpretability, alongside efficiency. We use the bit-cipher algorithm
to train word vectors via a two-step process that critically relies on a
hyperparameter -- bits -- that controls the vector dimension. While the first
step trains the bit-cipher, the second utilizes it under two different
aggregation modes -- summation or concatenation -- to produce contextually rich
representations from word co-occurrences. We extend our investigation into
bit-cipher's efficacy, performing probing experiments on part-of-speech (POS)
tagging and named entity recognition (NER) to assess its competitiveness with
classic embeddings like word2vec and GloVe. Additionally, we explore its
applicability in LM training and fine-tuning. By replacing embedding layers
with cipher embeddings, our experiments illustrate the notable efficiency of
cipher in accelerating the training process and attaining better optima
compared to conventional training paradigms. Experiments on the integration of
bit-cipher embedding layers with Roberta, T5, and OPT, prior to or as a
substitute for fine-tuning, showcase a promising enhancement to transfer
learning, allowing rapid model convergence while preserving competitive
performance.
