NLP Coherence and Style: How LLMs Form Sentences

Large language models do not assemble sentences from a grammar textbook. They extend a token sequence one piece at a time. Coherence is what you get when each next piece fits the pattern the model learned from vast text — and when later training and prompting steer which patterns win.

This page is the language twin of the vision spine: bind meaning in a shared space, then show it as readable words.

1. How output coheres

Tokens, not “words only”

Text is cut into tokens (subwords, punctuation, sometimes whole words). The model’s vocabulary is that token set. Generation is: given the tokens so far, score every next token, then sample or pick.

Autoregressive loop

flowchart TD
  Ctx["Context tokens<br/>system + user + prior reply"] --> Scores["Model scores<br/>next-token logits"]
  Scores --> Filter["Optional filters<br/>temperature, top-p, bans"]
  Filter --> Pick["Choose one token"]
  Pick --> Append["Append to context"]
  Append --> More{"Stop?"}
  More -->|no| Scores
  More -->|yes| Text["Surface as text"]

Each step conditions on everything already written in the window (and on tools/results if the product injects them). Local grammar, topic continuity, and “sounding like a reply” emerge because those patterns were common and rewarded in training — not because a separate syntax module ran first.

Attention as glue

Inside the network, attention lets each new token look back at earlier ones (and, in some designs, at other modalities). That is the main coherence mechanism: “the pronoun refers to…”, “this clause continues the list…”, “this answer should match the question’s constraints…”.

NLP stack under the hood (compressed)

Layer	Job
Tokenization	Text ↔ discrete ids
Embedding	Ids → vectors
Transformer blocks	Contextualize via attention + feed-forward
LM head	Vectors → next-token scores
Decoding policy	Scores → chosen token (greedy, sample, beam, …)

Classic NLP (parsers, taggers, entity linkers) still appears in products as tools or evals. The fluent surface of a chat model is mostly the generative loop above.

2. How a sentence actually forms

1. Prompt packing — system rules, user text, few-shot examples, and prior turns become one token stream.
2. Left-to-right commitment — once a token is chosen, later tokens must live with it (unless the product rewrites or regenerates).
3. Distributional grammar — subject–verb agreement, clause shape, and register appear because alternatives scored lower under this context.
4. Discourse habits — openings, hedging, lists, and closings reflect post-training preferences as much as raw pretraining.
5. Stop — end-of-sequence, max length, or a product rule ends the loop; the detokenizer turns ids back into characters.

So “sentence making” is sequential decision-making under a learned distribution, shaped by decoding settings and by whatever the product put in the prompt.

3. Why frontier labs sound different

Same broad architecture family; different data, objectives, and product defaults.

Lever	What it changes
Pretraining mix	Topics, languages, code vs prose, citation habits
Tokenizer	Word splits, multilingual behavior, “weird” spacing
Supervised fine-tune (SFT)	Task formats, helpfulness templates, refusal shape
Preference / RL stage (RLHF, RLAIF, DPO, …)	What “good” answers feel like: warmth, caution, length, humor
System / developer prompts	Standing voice the user never sees
Safety and policy layers	What gets refused, rewritten, or softened
Decoding defaults	Temperature, top-p, repetition penalties → dry vs lively
Tool and retrieval wiring	Whether answers cite, browse, or stay closed-book

Labs optimize for different product goals (assistant, search companion, coding agent, uncensored research). Those goals show up as word choice, sentence length, hedging, emoji habits, and structure — not as a separate “style neural net” you can point to.

Two models can share an architecture paper and still diverge because their preference data and system prompts disagree about what a good paragraph is.

4. How you ask for a specific style

Style is steered most reliably by clear constraints + examples, not by a single adjective.

Put the voice in the standing instructions

• Role: “You are a terse staff engineer writing for skimmers.”
• Register: “Plain words. Contractions. No corporate filler.”
• Shape: “Lead with the answer. Max two short paragraphs unless I ask for steps.”
• Negatives: “Do not open with Certainly. Do not use bulleted lists unless I ask.”

Standing system/custom instructions beat repeating the same plea every turn.

Show, don’t only name

Few-shot beats vibe words:

• Paste 3–5 lines that sound right and say “Match this voice.”
• Or paste a bad line and say “Never sound like this.”

Named styles (“like a BBC explainer”, “like a commit message”) work when the model has seen that genre; examples lock the grain.

Control knobs that are not “prompt poetry”

Ask for…	Example
Length	“Five sentences.” / “One screen, no scroll.”
Structure	“Claim, then evidence, then risk.”
Audience	“Reader knows git, not ML.”
Formality	“Board memo” vs “Slack to a teammate”
Dialect / language	“Swedish with technical terms in English”
Sampling	Lower temperature for tighter, more repetitive-safe prose

Multimodal and agent products

When tools run, style instructions still apply to the final wording, but facts should follow tool results. Say so: “Keep this voice; prefer tool output over memory for numbers.”

5. Tie-back to the prototype pattern

The tiny LSTM in this repo already shows the core: next-token prediction + sampling. Production labs add scale, preference training, and product prompts. The mechanism you can hold in your head stays the same: each token is a bet on what should come next under this context.

See (read the prompt) → bind (latent/context) → show (decoded tokens) so the reply teaches the idea — or the voice — without a lecture about the model.

Run

File	What it does
style_sampler.py	Bigram next-token loop with temperature + top-p; two corpora (warm / terse)
token_sampler.html	Same decoding knobs in the browser (character bigram)

# Decoding policy only — no torch, no training loop
python nlp/style_sampler.py
python nlp/style_sampler.py --corpus terse --prompt "State" --temp 0.4
python nlp/style_sampler.py --compare-temp

# Browser
# Linux: xdg-open nlp/token_sampler.html

Full train + generate (LSTM, embeddings, loss): llm/simple_llm_prototype.py and llm/README.md.

WHAT YOU JUST SAW

Each token was chosen left to right from a distribution shaped by prior tokens. Temperature flattened or sharpened that distribution; top-p cut the long tail. Switch corpus (warm vs terse) and the same mechanism sounds different — style lives in the data and the decoding knobs, not a separate grammar engine.

For the full loop (tokenize → embed → predict → sample → append), use the LSTM prototype in llm/.

Connect this to the tiny next-token loop