Alberto1231/community_alignment_modified

Community Alignment Modified: next-human followups

This is a deterministic next-human-turn view of `facebook/community-alignment-dataset` at pinned revision 97343c7f6399fcbea430ed0f37c1768281a78d56. It contains 2,514 eligible conversations from 90,256 source rows.

Five fixed rows are published only as fewshot_demonstrations. Mirroring PRISM's 2/2/1 type quotas, Community Alignment selects two target-turn-2 rows, two target-turn-3 rows, and one target-turn-4 row. Targets contain 30--200 words and each prompt+target pair uses at most 256 MARIN tokens. Selection within each stratum is hash-randomized with seed 42; selected rows are removed from the query pool rather than rerolled after inspection. The remaining 2,509 disjoint rows are published as source_examples. Finetuning and validation prepend the five fixed fewshot_demonstrations prompt+target demonstrations to each query using the same delimiter and task instruction as lm-eval.

The published validation split performs that formatting once during dataset construction. First, the shortest random-order prefix of complete source examples reaching 650,000 source tokens is selected without counting demonstrations. It contains 650,269 source tokens. Then the same five demonstrations are prepended to every selected query, yielding 3,194,395 processed tokens after also counting one EOS boundary per document. In this split, prompt stores the exact lm-eval context without the held-out target, while text appends the target for full-document causal-LM validation loss. The original query-only values remain in source_examples under the same conversation_id. Runtime evaluation does not resample rows or reconstruct demonstrations.

source_examples contains complete, source-derived next-human examples before the five-shot prefix is added. Each text value contains the task instruction, every available context turn used at the prediction boundary, and the genuine human target; it is not a prompt-only or fragmented representation.

Each source conversation contributes at most one example. target is the deepest available human follow-up that has at least one complete preceding human/preferred-assistant exchange. Candidate assistant answers to the target turn are excluded, preventing target-answer leakage.

Next-human-turn representation

• —prompt begins exactly with Conversation: and ends exactly with Human: .
• —target is the source human follow-up, retained byte-for-byte.
• —Earlier human prompts and preferred assistant responses are retained byte-for-byte and rendered with Human: / Assistant: labels.
• —text is Continue the conversation by writing exactly one next message from the human participant. Return only that message; do not include a speaker label, explanation, or additional turn. followed by prompt + target; causal-LM loss can be applied to the entire field.
• —messages, training_messages, preference keys, source identity, language, and annotator metadata are included for auditing.

No source utterance is paraphrased, trimmed, or normalized. The transformation only chooses the prediction boundary and adds explicit speaker labels and an open human cue for next-human-turn validation.

PRISM-compatible filtering

• —Only source rows whose assigned_lang is English and whose complete combined conversation is classified as English by pinned langid==1.1.6 are published.
• —Exact normalized model-facing conversations are unique. Grouping uses Unicode NFKC, case-folding, and collapsed whitespace. One source row is selected at random per duplicate group with seed 42; group ordering uses an independent seeded hash so repeated source records do not receive extra sampling probability.
• —prompt is at most 400 tokens.
• —target is at most 256 tokens.
• —Complete task-formatted text is at most 2048 tokens.
• —Lengths use `marin-community/isoflop-1ep20-d2304-L23-B128-dclm-default-4e21eb` at c932b4da5f56c721082e1a678ecfb8c0f4e9d4c7 with add_special_tokens=False, matching PRISM Trial 3's tokenizer and directly applicable per-row caps.
• —No demographic, language, or conversation-type balancing is performed.

The upstream dataset is released under CC BY 4.0. Users should consult its dataset card for collection details, intended use, and limitations.