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Antigravity Agent
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id: wiki-2026-0508-sequence-to-sequence-models
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title: Sequence to Sequence Models
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [seq2seq, Encoder-Decoder, Sequence Modeling, Sequence-to-Sequence]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [architecture, nlp, transformer, encoder-decoder]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: python
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framework: PyTorch / Transformers
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---
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# Sequence to Sequence Models
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## 매 한 줄
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> **"매 input sequence → output sequence — 매 길이 다른 변환"**. 매 Sutskever (2014) RNN encoder-decoder → Bahdanau (2015) attention → Vaswani (2017) Transformer 의 진화. 매 2026: 거의 모든 generative LLM (GPT, Claude, Gemini) 이 매 decoder-only seq2seq, 매 T5/BART 같은 encoder-decoder 는 specific task (번역, summarization fine-tune) 에 잔존.
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## 매 핵심
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### 매 Architecture family
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- **RNN encoder-decoder** (2014): 매 historical, vanishing gradient, no attention.
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- **Attention seq2seq** (2015): 매 alignment 학습 — 번역 quality 점프.
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- **Transformer encoder-decoder** (2017): 매 self-attention, parallelizable. T5, BART, mT5.
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- **Decoder-only** (2018+): GPT family. 매 LLM 의 dominant pattern.
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- **Encoder-only** (BERT): classification/embedding, generation 아님.
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### 매 핵심 컴포넌트
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- Tokenizer (BPE, SentencePiece, tiktoken).
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- Embedding + positional encoding (RoPE, ALiBi 2026 표준).
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- Self-attention / cross-attention.
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- Teacher forcing for training, autoregressive decoding for inference.
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### 매 Decoding 전략
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- Greedy / Beam search — 매 deterministic task.
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- Sampling (temperature, top-p, top-k, min-p) — 매 creative.
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- Speculative / Medusa — 매 inference 가속.
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- Constrained / structured (JSON schema) — 매 tool use.
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### 매 응용
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1. Machine translation (NLLB, M2M-100).
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2. Summarization (BART, Pegasus).
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3. Code generation (Claude Code, Copilot).
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4. Speech (Whisper encoder + decoder).
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5. Image captioning, VQA (multimodal seq2seq).
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## 💻 패턴
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### Tiny Transformer encoder-decoder
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```python
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import torch.nn as nn
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class Seq2Seq(nn.Module):
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def __init__(self, vocab, d=256, nhead=4, nl=4):
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super().__init__()
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self.emb_s = nn.Embedding(vocab, d)
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self.emb_t = nn.Embedding(vocab, d)
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self.tx = nn.Transformer(d, nhead, nl, nl, batch_first=True)
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self.out = nn.Linear(d, vocab)
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def forward(self, src, tgt):
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return self.out(self.tx(self.emb_s(src), self.emb_t(tgt)))
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```
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### HF Transformers (T5)
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```python
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from transformers import T5ForConditionalGeneration, T5Tokenizer
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tok = T5Tokenizer.from_pretrained("t5-base")
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m = T5ForConditionalGeneration.from_pretrained("t5-base")
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inp = tok("translate English to German: Hello world", return_tensors="pt").input_ids
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print(tok.decode(m.generate(inp)[0], skip_special_tokens=True))
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```
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### Decoder-only generation (Claude API)
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```python
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import anthropic
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c = anthropic.Anthropic()
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msg = c.messages.create(
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model="claude-opus-4-7",
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max_tokens=1024,
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messages=[{"role": "user", "content": "Summarize: ..."}],
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)
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print(msg.content[0].text)
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```
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### Beam search decode
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```python
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out = model.generate(input_ids, num_beams=4, length_penalty=0.6,
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no_repeat_ngram_size=3, max_new_tokens=128)
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```
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### Streaming
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```python
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with c.messages.stream(model="claude-opus-4-7", max_tokens=512,
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messages=msgs) as s:
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for text in s.text_stream:
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print(text, end="", flush=True)
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```
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### KV cache reuse
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```python
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out = model(**inputs, use_cache=True, past_key_values=pkv)
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pkv = out.past_key_values # 매 next step 에 재사용
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```
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## 매 결정 기준
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| 상황 | Approach |
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| General LLM task | decoder-only (Claude, GPT) |
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| Specific translation/summarization fine-tune | T5/BART encoder-decoder |
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| Embedding / classification | encoder-only (BERT family) |
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| Speech-to-text | Whisper-style enc-dec |
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| Long sequences, low cost | Mamba / Hybrid seq2seq |
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**기본값**: decoder-only LLM via API.
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## 🔗 Graph
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- 부모: [[Deep Learning]] · [[NLP]]
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- 변형: [[Transformer]] · [[Selective State Space Models (Mamba)]] · [[Encoder-Decoder]]
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- 응용: [[Summarization]] · [[Code-Generation]]
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- Adjacent: [[Attention Mechanism]] · [[Tokenization]]
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## 🤖 LLM 활용
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**언제**: input → output 변환 task 정의 가능. 매 API call 로 충분.
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**언제 X**: pure classification — encoder + head 가 매 더 cheap.
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## ❌ 안티패턴
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- **Greedy for creative**: repetition. 매 sampling 사용.
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- **No cache**: O(L²) inference. 매 KV cache 필수.
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- **Train from scratch**: 매 거의 항상 잘못된 선택. Fine-tune 또는 prompt.
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## 🧪 검증 / 중복
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- Verified (Sutskever 2014, Vaswani 2017, HF Transformers docs).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — full seq2seq family 2026 |
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