from __future__ import annotations import argparse from pathlib import Path from typing import Any from src.analysis.compare_predictions import load_prediction_map, row_id from src.data.io_utils import read_jsonl, write_csv SPECS = { "vifactcheck": { "label": "ViFactCheck", "split": "test", "baseline_pred": Path("outputs/baselines/vifactcheck/encoder_verifier/xlm-roberta-large/seed_13/predictions_test.jsonl"), "wikikg_pred": Path("outputs/verifier/vifactcheck/wikikg_fact/xlm-roberta-large_wikikg_text_only_diag_top5/seed_13/predictions_test.jsonl"), "retrieval_topk": Path("outputs/retrieval/vifactcheck/wikikg_topk_test.jsonl"), "verified_subgraphs": Path("outputs/kg/vifactcheck/verified_claim_subgraphs_test.jsonl"), "unsupported_triples": Path("outputs/kg/vifactcheck/unsupported_triples_test.jsonl"), }, "averitec": { "label": "AVeriTeC", "split": "local_test", "baseline_pred": Path("outputs/llm_baselines/averitec/gemma4_31b_q4/local_test_predictions.jsonl"), "wikikg_pred": Path("outputs/llm_baselines/averitec/gemma4_31b_q4_qa_wikikg_paths_only_top10_kg5/local_test_predictions.jsonl"), "retrieval_topk": Path("outputs/retrieval/averitec/wikikg_topk_local_test.jsonl"), "verified_subgraphs": Path("outputs/kg/averitec/verified_claim_subgraphs_local_test.jsonl"), "unsupported_triples": Path("outputs/kg/averitec/unsupported_triples_local_test.jsonl"), }, "healthver": { "label": "HealthVer", "split": "test", "baseline_pred": Path("outputs/baselines/healthver/encoder_verifier/microsoft__BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext/seed_13/predictions_test.jsonl"), "wikikg_pred": Path("outputs/verifier/healthver/wikikg_fact/microsoft__BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext_wikikg_top5/seed_13/predictions_test.jsonl"), "retrieval_topk": Path("outputs/retrieval/healthver/wikikg_topk_test.jsonl"), "verified_subgraphs": Path("outputs/kg/healthver/verified_claim_subgraphs_test.jsonl"), "unsupported_triples": Path("outputs/kg/healthver/unsupported_triples_test.jsonl"), }, } BIOMEDICAL_RELATIONS = {"TREATS", "PREVENTS", "CAUSES", "INCREASES_RISK", "DECREASES_RISK"} def load_map(path: Path) -> dict[str, dict[str, Any]]: return {row_id(row): row for row in read_jsonl(path)} def group_rows(path: Path) -> dict[str, list[dict[str, Any]]]: grouped: dict[str, list[dict[str, Any]]] = {} for row in read_jsonl(path): grouped.setdefault(str(row.get("claim_id", "")), []).append(row) return grouped def is_wrong_relation(row: dict[str, Any]) -> bool: flags = set(row.get("removal_reasons") or []) | set(row.get("rule_notes") or []) return "relation_demoted_to_associated" in flags or bool(row.get("relation_original")) or row.get("nli_label") == "entailment" def build_claim_rows(dataset: str) -> list[dict[str, Any]]: spec = SPECS[dataset] baseline = load_prediction_map(spec["baseline_pred"]) wikikg = load_prediction_map(spec["wikikg_pred"]) topk = load_map(spec["retrieval_topk"]) subgraphs = load_map(spec["verified_subgraphs"]) unsupported = group_rows(spec["unsupported_triples"]) rows: list[dict[str, Any]] = [] for claim_id in sorted(wikikg): pred = wikikg[claim_id] base = baseline.get(claim_id, {}) subgraph = subgraphs.get(claim_id, {}) metrics = (topk.get(claim_id) or {}).get("metrics", {}) unsupported_rows = unsupported.get(claim_id, []) correct = str(pred.get("prediction")) == str(pred.get("gold")) retrieval_miss = False if dataset == "healthver": retrieval_miss = (not correct) and int(subgraph.get("num_triples") or 0) == 0 and int(subgraph.get("num_facts") or 0) == 0 else: retrieval_miss = (not correct) and not bool(metrics.get("gold_at_10")) wrong_relation = (not correct) and any(is_wrong_relation(row) for row in unsupported_rows) overclaim_biomedical = (not correct) and dataset == "healthver" and any( row.get("relation") in BIOMEDICAL_RELATIONS or row.get("relation_original") in BIOMEDICAL_RELATIONS or "relation_demoted_to_associated" in set(row.get("removal_reasons") or []) | set(row.get("rule_notes") or []) for row in unsupported_rows ) gold = str(pred.get("gold", "")) prediction = str(pred.get("prediction", "")) nei_confusion = (not correct) and ("NEI" in {gold, prediction}) conflicting_confusion = dataset == "averitec" and (not correct) and ("CONFLICTING" in {gold, prediction}) weak_explanation = correct and int(subgraph.get("num_triples") or 0) == 0 and int(subgraph.get("num_facts") or 0) == 0 good_kg_path_wrong_verdict = (not correct) and ( int(subgraph.get("num_triples") or 0) > 0 or int(subgraph.get("num_facts") or 0) > 0 ) rows.append( { "dataset": dataset, "split": spec["split"], "claim_id": claim_id, "gold": gold, "baseline_prediction": base.get("prediction", ""), "wikikg_prediction": prediction, "baseline_correct": str(base.get("prediction", "")) == gold if base else "", "wikikg_correct": correct, "retrieval_miss": int(retrieval_miss), "wrong_kg_relation": int(wrong_relation), "overclaim_biomedical_relation": int(overclaim_biomedical), "nei_confusion": int(nei_confusion), "conflicting_confusion": int(conflicting_confusion), "correct_label_weak_explanation": int(weak_explanation), "good_kg_path_wrong_verdict": int(good_kg_path_wrong_verdict), "num_verified_facts": int(subgraph.get("num_facts") or 0), "num_verified_triples": int(subgraph.get("num_triples") or 0), "num_unsupported_triples": len(unsupported_rows), } ) return rows def first_example(rows: list[dict[str, Any]], field: str) -> str: for row in rows: if int(row[field]) == 1: return row["claim_id"] return "" def summarize(rows: list[dict[str, Any]]) -> list[dict[str, Any]]: by_dataset: dict[str, list[dict[str, Any]]] = {} for row in rows: by_dataset.setdefault(row["dataset"], []).append(row) def count(dataset: str, field: str) -> int: return sum(int(row[field]) for row in by_dataset.get(dataset, [])) def example(field: str, note: str) -> str: ids = [] for dataset in ("vifactcheck", "averitec", "healthver"): claim_id = first_example(by_dataset.get(dataset, []), field) if claim_id: ids.append(f"{SPECS[dataset]['label']}:{claim_id}") suffix = "; ".join(ids[:2]) return note if not suffix else f"{note}; e.g. {suffix}" return [ { "Error type": "Retrieval miss", "ViFactCheck": count("vifactcheck", "retrieval_miss"), "AVeriTeC": count("averitec", "retrieval_miss"), "HealthVer": count("healthver", "retrieval_miss"), "Example / interpretation": example("retrieval_miss", "evidence absent or still ranked too low"), }, { "Error type": "Wrong KG relation", "ViFactCheck": count("vifactcheck", "wrong_kg_relation"), "AVeriTeC": count("averitec", "wrong_kg_relation"), "HealthVer": count("healthver", "wrong_kg_relation"), "Example / interpretation": example("wrong_kg_relation", "relation semantics remain too strong or misaligned"), }, { "Error type": "Overclaim biomedical relation", "ViFactCheck": "n/a", "AVeriTeC": "n/a", "HealthVer": count("healthver", "overclaim_biomedical_relation"), "Example / interpretation": example("overclaim_biomedical_relation", "biomedical claims still risk over-strong causal wording"), }, { "Error type": "NEI confusion", "ViFactCheck": count("vifactcheck", "nei_confusion"), "AVeriTeC": count("averitec", "nei_confusion"), "HealthVer": count("healthver", "nei_confusion"), "Example / interpretation": example("nei_confusion", "insufficient evidence still flips into support or refute"), }, { "Error type": "CONFLICTING confusion", "ViFactCheck": "n/a", "AVeriTeC": count("averitec", "conflicting_confusion"), "HealthVer": "n/a", "Example / interpretation": example("conflicting_confusion", "cherry-picking cases remain the hardest label"), }, { "Error type": "Correct label, weak explanation", "ViFactCheck": count("vifactcheck", "correct_label_weak_explanation"), "AVeriTeC": count("averitec", "correct_label_weak_explanation"), "HealthVer": count("healthver", "correct_label_weak_explanation"), "Example / interpretation": example("correct_label_weak_explanation", "prediction correct despite little retained KG support"), }, { "Error type": "Good KG path, wrong verdict", "ViFactCheck": count("vifactcheck", "good_kg_path_wrong_verdict"), "AVeriTeC": count("averitec", "good_kg_path_wrong_verdict"), "HealthVer": count("healthver", "good_kg_path_wrong_verdict"), "Example / interpretation": example("good_kg_path_wrong_verdict", "useful path exists but the verifier still misclassifies"), }, ] def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--analysis-output", type=Path, default=Path("outputs/analysis/error_analysis.csv")) parser.add_argument("--table-output", type=Path, default=Path("outputs/tables/T15_error_analysis.csv")) args = parser.parse_args() rows: list[dict[str, Any]] = [] for dataset in ("vifactcheck", "averitec", "healthver"): rows.extend(build_claim_rows(dataset)) write_csv(args.analysis_output, rows) write_csv(args.table_output, summarize(rows)) print(f"Wrote {len(rows)} per-claim error rows to {args.analysis_output}") if __name__ == "__main__": main()