Papers › Repeat After Me: Transformers are Better than State Space Models at Copying

Repeat After Me: Transformers are Better than State Space Models at Copying

1 Feb 2024arXiv:2402.01032archive 2025-07-28

Samy Jelassi, David Brandfonbrener, Sham M. Kakade, Eran Malach

Transformers are the dominant architecture for sequence modeling, but there is growing interest in models that use a fixed-size latent state that does not depend on the sequence length, which we refer to as "generalized state space models" (GSSMs). In this paper we show that while GSSMs are promising in terms of inference-time efficiency, they are limited compared to transformer models on tasks that require copying from the input context. We start with a theoretical analysis of the simple task of string copying and prove that a two layer transformer can copy strings of exponential length while GSSMs are fundamentally limited by their fixed-size latent state. Empirically, we find that transformers outperform GSSMs in terms of efficiency and generalization on synthetic tasks that require copying the context. Finally, we evaluate pretrained large language models and find that transformer models dramatically outperform state space models at copying and retrieving information from context. Taken together, these results suggest a fundamental gap between transformers and GSSMs on tasks of practical interest.

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arr_to_str sjelassi/transformers_ssm_copy/synthetic_exps/data_utils.py official repository ran fingerprinted MIT (permissive) · 0d92dcc623352c2c · report
build_mpt_alibi_tensor sjelassi/transformers_ssm_copy/synthetic_exps/models/alibi_model.py official repository ran MIT (permissive) · f25aff9e401bdd61 · report
ce_loss sjelassi/transformers_ssm_copy/synthetic_exps/train_utils.py official repository ran MIT (permissive) · b9b929da44e45018 · report
custom_get_scheduler sjelassi/transformers_ssm_copy/synthetic_exps/train_utils.py official repository ran MIT (permissive) · 72642a4d8a990cb5 · report
exact_match_score sjelassi/transformers_ssm_copy/pretrained_exps/qa_evaluation_utils.py official repository ran fingerprinted MIT (permissive) · 67b1c1bbca4316ab · report
f1_score sjelassi/transformers_ssm_copy/pretrained_exps/qa_evaluation_utils.py official repository ran fingerprinted MIT (permissive) · 933e94e11554ccb1 · report
generate_str_unique_ngram sjelassi/transformers_ssm_copy/synthetic_exps/data_utils.py official repository ran MIT (permissive) · 0314589d134038a1 · report
get_optimizer sjelassi/transformers_ssm_copy/synthetic_exps/train_utils.py official repository ran MIT (permissive) · 32a6a2b055feb482 · report
normalize_answer sjelassi/transformers_ssm_copy/pretrained_exps/qa_evaluation_utils.py official repository ran fingerprinted MIT (permissive) · c29fc928dae85749 · report
rand_num sjelassi/transformers_ssm_copy/synthetic_exps/data_utils.py official repository ran MIT (permissive) · b80507bc684ce720 · report

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