https://nalar.dev/tags/autoregressive-models/ Recent content in Autoregressive Models on Nalar Hugo en-us Wed, 16 Sep 2026 00:00:00 +0700 https://nalar.dev/account-for-exposure-bias-in-autoregressive-decoding/ Wed, 16 Sep 2026 00:00:00 +0700 https://nalar.dev/account-for-exposure-bias-in-autoregressive-decoding/ <p>An autoregressive model can receive cleaner context during training than it receives during generation. Under teacher forcing, the next-token prediction is conditioned on a reference prefix from the training sequence. During free-running decoding, the model instead conditions on tokens it generated itself. Once a generated token differs from the intended continuation, later predictions operate on a prefix that training may have represented less often.</p> <p>This mismatch is commonly called exposure bias. It is not simply a claim that autoregressive models make errors. The specific issue is that the distribution of prefixes presented to the model can change between optimization and generation, and an early deviation can change every subsequent conditional prediction.</p>