iRanadheer/flicc-fallacy-sft

FLICC fallacy detection — SFT data

Reasoning traces for training a student model on the twelve FLICC reasoning fallacies of Zanartu et al. 2024 (doi.org/10.1038/s41598-024-76139-w).

Layout

path	rows	assistant turn
recot/train.jsonl	1614	full deconstruction trace + YAML
recot/train_eval.jsonl	177	full deconstruction trace + YAML
labels/train.jsonl	1614	YAML answer only
labels/train_eval.jsonl	177	YAML answer only

Both arms come from one teacher pass over the same rows with the same stratified split (seed 42), so they differ only in whether the reasoning is present. That is the ablation for the source paper's own thesis: is the manual deconstruction what makes the dataset valuable, or do the labels alone suffice?

split_manifest.json in each folder records the seed, the rule, and which itemId went to which split, so the split is checkable without these files.

Decontamination

5 of the 1,796 training rows are excluded here. Normalising away punctuation and case, then confirming by character diff, three training rows are >=98% identical to an evaluation row:

training row	eval row	difference
train-0605	test-0079	one capital letter, a trailing period
train-1390	val-0382	curly quotes vs a period
train-1599	val-0113	one apostrophe

Two further rows, train-0223 and train-0611, are the same sentence carrying different gold labels (single cause and misrepresentation), so the teacher produced contradictory traces for one input.

The same conflict exists inside the validation split (val-0108 /val-0386, false equivalence vs single cause) and is left untouched — correcting someone else's evaluation annotation is not appropriate; it is reported as label noise instead.

Provenance

Source rows are the FLICC training split, vendored verbatim from github.com/fzanart/FLICC. Only the training split — no val or test row appears here.

Traces were generated by a teacher LLM shown the gold label and asked to produce reasoning arriving at it without revealing it was told. Traces whose final label did not match gold were discarded, not repaired.

The system prompt is embedded per row and is the same prompt the student meets at inference. The user turn is the plain classification turn — it never contains the gold label or the teacher trigger.

Codebook

The definitions in the system prompt are not the paper's Table 1. They were rewritten from the training split, because Table 1 as published does not fully specify the annotation practice. On held-out test data this was worth +0.147 macro F1 on Claude Opus 4.7 and +0.191 on GPT-5.5 over the same prompt carrying Table 1 verbatim. See docs/decisions.md in the source repo.