“Language Modeling via Learning to Rank”, Arvid Frydenlund, Gagandeep Singh, Frank Rudzicz2021-10-13 (knowledge distillation; similar)⁠:

We consider language modeling (LM) as a multi-label structured prediction task by re-framing training from solely predicting a single ground-truth word to ranking a set of words which could continue a given context. To avoid annotating top-k ranks, we generate them using pre-trained LMs: GPT-2, BERT and Born-Again models. This leads to a rank-based form of knowledge distillation (KD). We also develop a method using N-grams to create a non-probabilistic teacher which generates the ranks without the need of a pre-trained LM.

We confirm the hypotheses that we can treat LMing as a ranking task and that we can do so without the use of a pre-trained LM. We show that rank-based KD generally improves perplexity (PPL), often with statistical-significance, when compared to Kullback-Leibler-based KD. Surprisingly, given the simplicity of the method, N-grams act as competitive teachers and achieve similar performance as using either BERT or a Born-Again model teachers. GPT-2 always acts as the best teacher, though, and using it and a Transformer-XL student on Wiki-02, rank-based KD reduces a cross-entropy baseline 65.27 → 55.94 and against a KL-based KD of 56.70.

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