# -*- coding: utf-8 -*- """Builds the published ClaimCheck evaluation dataset. Every case is run through the live implementation. The dataset carries three distinct judgements, because they answer three different questions: expected_status what ClaimCheck's documented rules say should happen claimcheck_status what the implementation actually returns human_verdict whether that outcome actually helps a user (acceptable / false_positive / false_negative) Conflating those is how verifier benchmarks end up flattering themselves. A row can be perfectly spec-conformant and still be a false positive. """ import json, os, sys, time HERE = os.path.dirname(os.path.abspath(__file__)) sys.path.insert(0, HERE) sys.path.insert(0, os.path.dirname(HERE)) import app from build_cases import C as AUTHORED OUT_DIR = os.path.join(HERE, "dataset", "data") os.makedirs(OUT_DIR, exist_ok=True) HUMAN = {"known_false_positive": "false_positive", "known_false_negative": "false_negative"} # Corrections applied to generated cases whose label was wrong about the # documented rules, keyed by focus_value. Each entry states the spec-conformant # status, the human verdict, and why. These are recorded here rather than edited # into the cases so the correction is auditable. OVERRIDES = { "meaningfully better": ("not_extracted", "acceptable", "Vague prose yields no claim at all; it lowers coverage instead of being scored."), "last year": ("not_extracted", "acceptable", "Relative dates never become claims: they cannot be resolved without a generation date."), "昨年": ("not_extracted", "acceptable", "相対表現は claim にならない。counts.relative_date_mentions に計上されるのみ。"), "2.": ("not_extracted", "acceptable", "List markers at a line start are excluded from numeric extraction."), "堅調に推移している": ("not_extracted", "acceptable", "A qualitative statement produces no checkable claim; this is what coverage is for."), "2023年": ("unsupported", "false_positive", "昭和48年 + 創業50周年 = 2023 is correct, but derivation is arithmetic over NUMBERS only - " "date arithmetic is not performed, so a correct answer is flagged. The answer-level verdict is retry."), 'Subscription revenue declined 8% year over year in the second quarter.': ("approximate", "false_negative", "grew -> declined is a semantic reversal, but character similarity is 0.97 so the quote reads as a " "paraphrase. The answer is still flagged (verdict=retry) because approximate does not count as grounded."), "国内売上高は前年同期比で7%減少した": ("approximate", "false_negative", "増加 -> 減少 differs by one character, so similarity stays high and the reversal is not called " "contradicted. The single most important limitation of character-level matching on Japanese."), "anyone still on release 4.2 or older must update without delay": ("unsupported", "false_positive", "A faithful paraphrase rewritten word for word falls below the similarity threshold."), "サービス障害に起因する間接的な損害について当社は一切の賠償責任を負わない": ("unsupported", "false_positive", "意味を保った法務的な言い換えだが、語をほぼ入れ替えているため閾値に届かない。"), "service credits apply in any calendar month that falls below 99.9% uptime": ("unsupported", "false_positive", "A faithful restatement scoring 0.61: below the approximate threshold, so a correct answer is flagged."), "データセンター向けの電力需要は前年度から約12%伸びた": ("contradicted", "false_positive", "増えた -> 伸びた は忠実な言い換えだが類似度0.81が approximate 閾値0.82をわずかに下回り、" "contradicted の帯に落ちる。閾値境界の典型的な偽陽性。"), "We opened 21 new distribution centres in 2025.": ("approximate", "acceptable", "The quote status understates it, but the NUMERIC claim 21 inside the quote is unsupported, so the " "layered design still flags the answer. Read the claims inside a quote, not only the quote status."), "本件は民法第719条に基づく不法行為に当たる": ("approximate", "acceptable", "第709条 -> 第719条 は QUOTE では approximate に留まるが、ENTITY 第719条 が contradicted を返すため " "多層で捕捉できている。"), } cases = [] for c in AUTHORED: d = dict(c); d["source"] = "authored"; cases.append(d) with open(os.path.join(HERE, "workflow_cases.json"), encoding="utf-8") as f: for c in json.load(f): d = {k: c[k] for k in ("language", "claim_type", "category", "context", "answer", "focus_value", "expected_status", "rationale") if k in c} d["source"] = "generated" cases.append(d) # Deduplicate on (context, answer, focus_value). seen, uniq = set(), [] for c in cases: k = (c["context"], c["answer"], c["focus_value"]) if k in seen: continue seen.add(k) uniq.append(c) dropped_dupes = len(cases) - len(uniq) cases = uniq rows, bad_focus = [], [] for i, c in enumerate(cases): cid = "cc-%03d" % i focus = c.get("focus_value") or "" if focus and focus not in c["answer"]: bad_focus.append((cid, focus)); continue t0 = time.perf_counter() res = app.gate(c["answer"], c["context"], "", "", "", "", "") latency = (time.perf_counter() - t0) * 1000.0 ver = res.get("verify") or {} claims = ver.get("claims") or [] # A literal can be claimed by more than one extractor: a model number inside # 「」 is both a QUOTE and an ENTITY. Resolve to the claim type the case is # actually about, or the row measures the wrong check. actual, evidence, nearest, sim = None, None, None, None if focus: want = c["claim_type"] ordered = ([cl for cl in claims if cl.get("type") == want] + [cl for cl in claims if cl.get("type") != want]) for cl in ordered: # exact value match if cl.get("value") == focus: actual, evidence = cl.get("status"), cl.get("evidence") nearest, sim = cl.get("nearest_context_value"), cl.get("similarity"); break if actual is None: # then containment for cl in ordered: v = str(cl.get("value") or "") if v and (v in focus or focus in v): actual, evidence = cl.get("status"), cl.get("evidence") nearest, sim = cl.get("nearest_context_value"), cl.get("similarity"); break if actual is None: actual = "not_extracted" ov = OVERRIDES.get(focus) expected = c["expected_status"] human = HUMAN.get(c["category"], "acceptable") rationale = c["rationale"] if ov: expected, human, rationale = ov[0], ov[1], ov[2] rows.append({ "case_id": cid, "source": c["source"], "language": c["language"], "claim_type": c["claim_type"], "category": c["category"], "context": c["context"], "answer": c["answer"], "focus_value": focus, "expected_status": expected, "claimcheck_status": actual, "agrees": bool(actual == expected), "human_verdict": human, "evidence": evidence, "nearest_context_value": nearest, "similarity": sim, "verdict": res.get("verdict"), "grounding_score": ver.get("grounding_score"), "coverage": ver.get("coverage"), "claims_total": (ver.get("counts") or {}).get("claims_total"), "sentences_unverified": (ver.get("counts") or {}).get("sentences_unverified"), "latency_ms": round(latency, 3), "rationale": rationale, }) if bad_focus: print("DROPPED (focus_value not a substring of answer):", bad_focus) path = os.path.join(OUT_DIR, "eval.jsonl") with open(path, "w", encoding="utf-8") as f: for r in rows: f.write(json.dumps(r, ensure_ascii=False) + "\n") from collections import Counter agree = sum(1 for r in rows if r["agrees"]) print("cases=%d (deduped %d) | spec-conformant=%d (%.1f%%)" % (len(rows), dropped_dupes, agree, 100.0*agree/len(rows))) print("sources:", dict(Counter(r["source"] for r in rows))) print("languages:", dict(Counter(r["language"] for r in rows))) print("human_verdict:", dict(Counter(r["human_verdict"] for r in rows))) print() print("conformance by claim type:") for t in ("NUMERIC", "DATE", "QUOTE", "ENTITY", "URL"): sub = [r for r in rows if r["claim_type"] == t] if sub: a = sum(1 for r in sub if r["agrees"]) print(" %-8s %3d/%3d (%.0f%%)" % (t, a, len(sub), 100.0*a/len(sub))) print() print("DISAGREEMENTS (spec label vs implementation):") for r in rows: if not r["agrees"]: print(" %-8s %-7s %-8s expected=%-14s actual=%-14s %r" % ( r["case_id"], r["source"][:7], r["claim_type"], r["expected_status"], r["claimcheck_status"], (r["focus_value"] or "")[:30])) print() print("ANSWER-LEVEL behaviour (the number that actually matters in production):") should_flag = [r for r in rows if r["category"] in ("hallucinated", "contradicted", "adversarial", "known_false_negative")] flagged = [r for r in should_flag if r["verdict"] != "pass"] print(" answers that should be flagged and were: %d/%d (%.0f%%)" % (len(flagged), len(should_flag), 100.0*len(flagged)/max(1, len(should_flag)))) clean = [r for r in rows if r["category"] in ("grounded", "derived")] passed = [r for r in clean if r["verdict"] == "pass"] print(" clean answers that passed: %d/%d (%.0f%%) [the rest were annotated, not blocked]" % (len(passed), len(clean), 100.0*len(passed)/max(1, len(clean)))) fp = [r for r in rows if r["human_verdict"] == "false_positive"] fn = [r for r in rows if r["human_verdict"] == "false_negative"] print(" known false positives: %d (%.1f%% of set) | known false negatives: %d" % (len(fp), 100.0*len(fp)/len(rows), len(fn))) lat = sorted(r["latency_ms"] for r in rows) print("\nlatency p50=%.2fms p95=%.2fms max=%.2fms" % (lat[len(lat)//2], lat[int(len(lat)*0.95)], lat[-1])) print("wrote", path)