A research team is planning experiments across two NLP tasks: a free-form abstractive summarisation task that must generate fluent novel sentences, and a token-level named-entity recognition task that labels each input token. They want to choose a transformer architecture family that naturally suits each task before fine-tuning. Which two pairings of architecture to task are most appropriate for these experiments? Select TWO.
- AUse a decoder-only autoregressive transformer for the abstractive summarisation task, because it generates output tokens one at a time conditioned on prior tokens. Correct
- BUse an encoder-only bidirectional transformer for the token-level named-entity recognition task, because it produces contextual per-token representations for classification. Correct
- CUse an encoder-only bidirectional transformer for the abstractive summarisation task, because its masked-language objective writes fluent multi-sentence summaries directly.
- DUse a decoder-only autoregressive transformer for the named-entity recognition task, because its causal masking gives each token full bidirectional context for labelling.
- EUse a convolutional sequence model for both tasks, because fixed-width filters capture all long-range dependencies that transformer attention captures.
Why A is correct: Decoder-only autoregressive models are built for left-to-right generation, which matches producing novel summary sentences token by token.
Why B is correct: Encoder-only bidirectional models give every token a context-rich representation, ideal for per-token labelling such as entity recognition.
Why C is wrong: Tempting since encoders understand text well, but encoder-only models lack an autoregressive decoder and cannot freely generate novel summary sentences.
Why D is wrong: Plausible because decoders are powerful, but causal masking hides future tokens, so it does not give the full bidirectional context entity labelling benefits from.
Why E is wrong: Convolutional filters have a limited receptive field and do not capture long-range dependencies as attention does, so this misrepresents the trade-off.