Language modeling is the task of predicting the next word or character in a document. This technique can be used to train language models that can further be applied to a wide range of natural language tasks like text generation, text classification, and question answering.

The common types of language modeling techniques involve:

  1. N-gram Language Models
  2. Neural Language Models

A model’s language modeling capability is measured using cross-entropy and perplexity. Some datasets to evaluate language modeling are:

  1. WikiText-103: The WikiText language modeling dataset is a collection of over 100 million tokens extracted from the set of verified Good and Featured articles on Wikipedia. Compared to the preprocessed version of Penn Treebank (PTB), WikiText-2 is over 2 times larger and WikiText-103 is over 110 times larger. The WikiText dataset also features a far larger vocabulary and retains the original case, punctuation and numbers - all of which are removed in PTB. As it is composed of full articles, the dataset is well suited for models that can take advantage of long term dependencies.
  2. One Billion Word: The One Billion Word dataset is a dataset for language modeling. The training/held-out data was produced from the WMT 2011 News Crawl data using a combination of Bash shell and Perl scripts.
  3. C4: C4 is a colossal, cleaned version of Common Crawl’s web crawl corpus. It was based on Common Crawl dataset: https://commoncrawl.org. It was used to train the T5 text-to-text Transformer models.

One of the most recent popular benchmarks to evaluate language modeling capabilities with extrinsic metrics is called SuperGLUE.

Some popular and notable state-of-the-art language models, include:

  1. GPT-3: GPT-3 is an autoregressive transformer model with 175 billion parameters. It uses the same architecture/model as GPT-2, including the modified initialization, pre-normalization, and reversible tokenization, with the exception that GPT-3 uses alternating dense and locally banded sparse attention patterns in the layers of the transformer, similar to the Sparse Transformer. A Sparse Transformer is a Transformer based architecture which utilizes sparse factorizations of the attention matrix to reduce time/memory to O(n*sqrt(n)).
  2. Codex: We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities. A distinct production version of Codex powers GitHub Copilot. On HumanEval, a new evaluation set we release to measure functional correctness for synthesizing programs from docstrings, our model solves 28.8% of the problems, while GPT-3 solves 0%
  3. BERT: BERT, or Bidirectional Encoder Representations from Transformers, improves upon standard Transformers by removing the unidirectionality constraint by using a masked language model (MLM) pre-training objective. The masked language model randomly masks some of the tokens from the input, and the objective is to predict the original vocabulary id of the masked word based only on its context. Unlike left-to-right language model pre-training, the MLM objective enables the representation to fuse the left and the right context, which allows us to pre-train a deep bidirectional Transformer. In addition to the masked language model, BERT uses a next sentence prediction task that jointly pre-trains text-pair representations.
  4. Flan: We show that instruction tuning – finetuning language models on a collection of tasks described via instructions – substantially boosts zero-shot performance on unseen tasks. We take a 137B parameter pretrained language model and instruction-tune it on over 60 NLP tasks verbalized via natural language instruction templates. We evaluate this instruction-tuned model, which we call FLAN, on unseen task types. FLAN substantially improves the performance of its unmodified counterpart and surpasses zero-shot 175B GPT-3 on 20 of 25 tasks that we evaluate. FLAN even outperforms few-shot GPT-3 by a large margin on ANLI, RTE, BoolQ, AI2-ARC, OpenbookQA, and StoryCloze. Ablation studies reveal that number of tasks and model scale are key components to the success of instruction tuning.
  5. ULM FiT: Universal Language Model Fine-tuning, or ULMFiT, is an architecture and transfer learning method that can be applied to NLP tasks. It involves a 3-layer AWD-LSTM architecture for its representations. The training consists of three steps: 1) general language model pre-training on a Wikipedia-based text, 2) fine-tuning the language model on a target task, and 3) fine-tuning the classifier on the target task. ASGD Weight-Dropped LSTM, or AWD-LSTM, is a type of recurrent neural network that employs DropConnect for regularization, as well as NT-ASGD for optimization - non-monotonically triggered averaged SGD - which returns an average of last iterations of weights.