title: Short-term memory in neural language models
abstract: When a language model is trained to predict natural language sequences, its prediction at each moment depends on a representation of prior context. Thus, language models require mechanisms to maintain and access memory. Although we design the architectural features of these models, we do not know how their memory systems are functionally organized via learning: what kind of information about the prior context can they retrieve? We reasoned that access to arbitrary individual tokens from the past could be computationally powerful, akin to the  working memory which is important for flexible cognition in humans, and we therefore tested whether language models could ``retrieve'' the exact words that occurred previously in a text. In particular, we tested how the ability to retrieve prior words depended on (i) the number of words being retrieved, (ii) their semantic coherence, and (iii) the length and quality of the intervening text. We evaluated two particular architectures of neural language models: the attention-based transformer and the long short-term memory network (LSTM). In our paradigm, language models processed English text in which a list of nouns occurred twice. We operationalized retrieval as the reduction in surprisal from the first presentation of the list to its second presentation. We found that the transformer models retrieved both the identity and ordering of nouns from the first list. The transformer was successful even when the noun lists were semantically incoherent, and this effect was largely robust to the type or length of the intervening text. Further, the transformer’s retrieval was markedly enhanced when it was trained on a larger corpus and with greater model depth. Lastly, its ability to index prior tokens was dependent on learned attention patterns. In contrast, the LSTM models exhibited less precise retrieval (smaller reductions in surprisal). The LSTM’s retrieval was limited to list-initial tokens, and occurred only across short intervening texts. Moreover, the LSTM's retrieval was not sensitive to the order of nouns and this non-specific retrieval improved when the list was semantically coherent. In sum, the transformer, when trained to predict linguistic tokens, implements something akin to a working memory system, as it could flexibly retrieve individual token representations across arbitrary delays. Conversely, the LSTM maintained a coarser and more rapidly-decaying semantic gist of prior tokens, weighted heavily toward the earliest items.  Thus, although the transformer and LSTM architectures were both trained to predict language sequences, only the transformer learned to flexibly index prior tokens.
authors: Kristijan Armeni Christopher Honey Tal Linzen
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