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RNN-like language models are getting renewed attention from NLP researchersin recent years and several models have made significant progress, whichdemonstrates performance comparable to traditional transformers. However, dueto the recurrent nature of RNNs, this kind of language model can only storeinformation in a set of fixed-length state vectors. As a consequence, theystill suffer from forgetfulness though after a lot of improvements andoptimizations, when given complex instructions or prompts. As the promptedgeneration is the main and most concerned function of LMs, solving the problemof forgetting in the process of generation is no wonder of vital importance. Inthis paper, focusing on easing the prompt forgetting during generation, weproposed an architecture to teach the model memorizing prompt during generationby synthetic gradient. To force the model to memorize the prompt, we derive thestates that encode the prompt, then transform it into model parametermodification using low-rank gradient approximation, which hard-codes the promptinto model parameters temporarily. We construct a dataset for experiments, andthe results have demonstrated the effectiveness of our method in solving theproblem of forgetfulness in the process of prompted generation. We will releaseall the code upon acceptance.

ProSG: Using Prompt Synthetic Gradients to Alleviate Prompt Forgetting of RNN-like Language Models