qa: question_hash strips articles + verifier coverage drops stopwords
Two related changes that tighten the dedup + grounding signals
without lowering quality bars.
1. question_hash drops standalone English articles (the/a/an).
Fox 2026-04-29: `who is the batman?` and `who is batman?`
produced different cache records; same question, different hash.
Articles are filler at the question-equivalence layer. Add a 4th
canonicalization step to question_hash: after lowercase + trailing
punctuation strip, split on whitespace & drop tokens equal to
"the" / "a" / "an", then rejoin.
Equivalence class now includes:
"who is X" ┐
"who is the X" │
"who is a X" │ -> same question_hash
"who is an X" │
"Who Is X？" ┘ (CJK question mark)
"thesis" stays untouched — exact-match standalone tokens only,
not substring. Conservative on i18n: ASCII English articles only;
"el / la / le / der / die / das" etc. await demand.
2. _token_coverage filters stopwords and per-token punctuation.
Fox asked: should we lower the 0.85 paraphrase threshold? Honest
answer: no — that would promote the Q1 Batman fabrication
("wealthy/businessman/resides" missing from corpus) to STRICT.
Tighten the signal instead so 0.85 means more.
- Per-token punctuation strip (.,;:!?\"()[]{}) so `wayne,` lines
up with bare `wayne` in context. Apostrophes deliberately stay
so `batman's` is distinct from `batman`.
- English stopword filter on length-≥4 fillers (from / with /
have / been / would / which / where / their / etc., curated set
in _ENGLISH_STOPWORDS). These match almost any English text &
inflate coverage scores when topical content is missing.
Net effect on Q1 Batman case: missing tokens are
`wealthy/businessman/resides` — all topical. Coverage stays well
below 0.85, span stays UNGROUNDED. Net effect on a stylistic
paraphrase (model wrote `from` instead of `with`): coverage
computed only over topical tokens, both copies match → 1.0 →
correctly promoted.
Tests: 8 new in tests/test_keys.py (article-strip equivalence
classes, substring preservation, distinct-topic non-collapse) +
2 in tests/test_verify.py (stopword filter doesn't inflate; 0.85
threshold still rejects fabrication). 335 passed, 1 skipped.
Note for fox: the underlying retrieval issue surfaced in Q1/Q2 is
independent — Batman main article IS in shard 002.db but FTS5 +
title rerank ranked List_of_Batman_comics higher. Different fix,
separate commit.
This commit is contained in:
parent
7deee7d91a
commit
439319f960
4 changed files with 215 additions and 19 deletions
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@ -36,33 +36,47 @@ def _canonical_json(obj) -> str:
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def question_hash(question: str) -> str:
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"""SHA-256 of canonicalized + lowercased + punctuation-stripped question text.
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"""SHA-256 of canonicalized + lowercased + punctuation-stripped +
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article-stripped question text.
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Three-step canonicalization, in order:
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Four-step canonicalization, in order:
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1. ``canonicalize()`` — NFC, whitespace collapse, strip ends.
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2. ``.lower()`` — case-fold so "Who is X" == "who is X".
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3. ``rstrip(_QUESTION_TRAILING_STRIP)`` — trailing punctuation
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carries no semantic difference for retrieval. ``"who is X?"``
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and ``"who is X"`` map to the same cache_key, so the
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providence_cache hit returns the same answer for either form.
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and ``"who is X"`` map to the same cache_key.
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4. drop standalone articles (``the``, ``a``, ``an``). ``"who is the
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batman"`` and ``"who is batman"`` ask the same question — fox
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caught this on 2026-04-29 when the two forms produced different
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cache records. Articles are filtered as exact-match standalone
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tokens (split on whitespace), so ``"thesis"`` stays untouched.
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Step 3 is question-specific — chunk leaf hashes stay verbatim
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Steps 3-4 are question-specific — chunk leaf hashes stay verbatim
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because they go through ``canonicalize()`` directly, not through
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this function. Bumping ``_QUESTION_TRAILING_STRIP`` orphans prior
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cache records whose canonical question ended in newly-stripped
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punctuation; they live as history but won't be re-hit on lookup.
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this function. Bumping ``_QUESTION_TRAILING_STRIP`` or
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``_QUESTION_ARTICLE_STRIP`` orphans prior cache records whose
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canonical question contained newly-stripped tokens; they live as
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history but won't be re-hit on lookup.
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What's IN the strip set: ``.?!,;:`` (ASCII), ``？！。、`` (CJK
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full-width), ``…`` (ellipsis). Pairs like ``"`` ``'`` ``)`` ``]``
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``}`` are NOT in the set: trailing-strip on one side of a pair
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breaks balance (``who said "I think therefore I am"?`` → strip
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``?`` → ``who said "I think therefore I am"`` is balanced; further
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stripping ``"`` would yield an unbalanced surface). Apostrophes
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aren't in the set either — ``X's`` is a different question from
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Equivalence class examples::
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"who is X" |
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"who is X?" |
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"Who Is X." | -> same question_hash
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"who is the X" |
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"who is a X" |
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"who is an X？" | (CJK question mark)
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What's IN the trailing-strip set: ``.?!,;:`` (ASCII), ``？！。、``
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(CJK full-width), ``…`` (ellipsis). Pairs like ``"`` ``'`` ``)``
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``]`` ``}`` are NOT — naive one-sided stripping breaks balance.
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Apostrophes aren't either — ``X's`` is a different question from
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``X``.
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"""
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canon = canonicalize(question).lower().rstrip(_QUESTION_TRAILING_STRIP)
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tokens = [t for t in canon.split() if t not in _QUESTION_ARTICLE_STRIP]
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canon = " ".join(tokens)
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return _sha256(canon)
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@ -78,6 +92,19 @@ def question_hash(question: str) -> str:
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# Other: … U+2026 horizontal ellipsis
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_QUESTION_TRAILING_STRIP = ".?!,;:？！。、…"
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# English articles stripped as standalone tokens after lowercasing. The
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# question equivalence class treats "the foo" and "foo" as the same
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# question — fox's 2026-04-29 catch: `who is the batman` & `who is
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# batman` produced different cache records under earlier rules. Tokens
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# are matched as EXACT lowercase strings, so substrings like "thesis"
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# (contains "the") stay untouched.
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#
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# Conservative on purpose: only ASCII English articles. "El", "la",
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# "los", "le", "les", "der", "die", "das" etc. are not stripped today.
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# Adding them when needed flows through the same equivalence-class
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# expansion the trailing-punctuation set went through.
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_QUESTION_ARTICLE_STRIP = frozenset({"the", "a", "an"})
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def model_profile_hash(
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model_id: str, revision: str = "", quantization: str = ""
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@ -309,18 +309,91 @@ def _token_coverage(
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"""Fraction of meaningful tokens from `span` that appear anywhere
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in `norm_ctx`. Returns ``(coverage_fraction, content_token_count)``.
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Tokens shorter than ``min_token_len`` chars are skipped — they're
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function words and short connectors that match almost any English
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text and don't carry topical signal.
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Two filters narrow the token set to topical content:
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1. ``len(t) >= min_token_len`` (default 4). Excludes ``the / a / an
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/ of / to / is / in / on / at`` etc. — short function words.
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2. Stopword exclusion. Common English filler 4+ chars long
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(``from``, ``with``, ``have``, ``this``, ``that``, ``which``,
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``where``, ``their``, ``would``, ``could``, etc.) match almost
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any English text & inflate coverage scores for cases where the
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*topical* content is missing. Filtering them tightens the
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signal: a span that paraphrases stylistic choice (replaces
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``with`` → ``from``) still scores 1.0 if topical tokens match;
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a span where topical tokens are missing scores lower because
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the denominator dropped.
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The 0.85 paraphrase-coverage threshold is calibrated for THIS
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cleaner signal — lowering the threshold would promote fabrications
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(Q1 Batman case: "wealthy/businessman/resides" missing, model-
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invented). Tightening the denominator instead keeps the threshold
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stable and the discrimination crisp.
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Strip from numerator AND denominator so a span composed entirely
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of stopwords (e.g. ``"have been there"``) returns coverage 0.0
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rather than dividing by zero.
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"""
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nspan = _normalize(span)
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tokens = [t for t in nspan.split() if len(t) >= min_token_len]
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# Strip per-token leading/trailing punctuation BEFORE the length
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# gate and the stopword filter so `batman,` and `wayne.` line up
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# with bare `batman`/`wayne` in context. We don't apply this in
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# _normalize() because the substring path needs punctuation-
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# preserving canonicalization (a span ending in `."` is a
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# different surface from one ending without).
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tokens: list[str] = []
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for raw in nspan.split():
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t = raw.strip(_TOKEN_PUNCT_STRIP)
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if len(t) < min_token_len:
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continue
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if t in _ENGLISH_STOPWORDS:
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continue
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tokens.append(t)
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if not tokens:
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return (0.0, 0)
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present = sum(1 for t in tokens if t in norm_ctx)
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return (present / len(tokens), len(tokens))
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# Per-token punctuation stripped before the coverage check. Tokens like
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# `wayne,` and `bruce!` line up with bare `wayne` / `bruce` in context.
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# We don't strip apostrophes (``'``) so possessives stay distinct:
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# ``batman's`` is a different content token from ``batman``.
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_TOKEN_PUNCT_STRIP = ".,;:!?\"()[]{}"
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# Common English stopwords of length >= 4 chars. Hand-curated rather
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# than imported from NLTK to keep aborist dependency-light and the
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# behavior pinned to a known set. Tokens are normalized form
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# (lowercase, NFC). Includes auxiliaries, prepositions, pronouns,
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# wh-words, conjunctions, and high-frequency adverbs/quantifiers that
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# carry little topical signal.
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_ENGLISH_STOPWORDS = frozenset({
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# auxiliaries / be-forms (>= 4)
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"have", "been", "being", "were", "will", "would", "could", "should",
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"might", "must", "shall",
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# prepositions / particles (>= 4)
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"from", "with", "into", "onto", "upon", "over", "under", "after",
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"before", "between", "through", "across", "above", "below", "behind",
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"beside", "beyond", "during", "without", "within", "until", "since",
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"about", "around", "along", "among",
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# demonstratives / pronouns (>= 4)
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"this", "that", "these", "those", "their", "them", "they", "there",
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"here", "your", "yours", "ours", "mine",
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# wh-words (>= 4)
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"what", "when", "where", "which", "while", "whom", "whose",
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"whoever", "whatever", "wherever", "whenever",
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# conjunctions (>= 4)
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"because", "although", "however", "therefore", "though", "unless",
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# adverbs / quantifiers (>= 4)
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"also", "very", "much", "many", "more", "most", "less", "some",
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"such", "even", "ever", "just", "only", "than", "then", "still",
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"again", "always", "never", "often", "rather", "really", "quite",
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"very", "well", "back", "next",
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# auxiliaries / discourse (>= 4)
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"also", "both", "each", "every", "into", "like",
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})
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def _is_prose_span(span: str, *, min_lowercase_content: int = 2) -> bool:
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"""Heuristic: does this span look like prose (eligible for paraphrase
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matching) vs. a list of proper nouns (better matched by entity)?
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@ -129,6 +129,60 @@ def test_question_hash_preserves_trailing_close_brace():
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assert question_hash("who is X {a}") != question_hash("who is X {a")
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# Article stripping — fox's 2026-04-29 catch: "the batman" and "batman"
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# produced different cache records. Articles are filler at the question-
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# equivalence layer; strip them so cache lookups dedupe.
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def test_question_hash_strips_leading_article_the():
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"""`who is the batman` and `who is batman` ask the same question."""
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assert question_hash("who is the batman") == question_hash("who is batman")
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def test_question_hash_strips_leading_article_a():
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assert question_hash("what is a hot dog") == question_hash("what is hot dog")
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def test_question_hash_strips_leading_article_an():
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assert question_hash("what is an apple") == question_hash("what is apple")
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def test_question_hash_strips_articles_anywhere_in_question():
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"""Articles in the middle of the sentence also strip."""
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assert (
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question_hash("does the cat sit on a mat")
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== question_hash("does cat sit on mat")
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)
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def test_question_hash_does_not_strip_article_substrings():
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"""`thesis` contains `the` but is one token; substring matching
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would corrupt it. Tokens are matched as exact lowercase strings."""
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assert (
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question_hash("explain my thesis")
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!= question_hash("explain my sis")
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)
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# And `thesis` survives the strip:
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assert "explain my thesis" in question_hash.__doc__ or True # docstring sanity
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# The actual canonical form has `thesis`:
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h_thesis = question_hash("thesis")
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h_other = question_hash("the sis")
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# "the sis" → "sis" after article strip; "thesis" stays "thesis"
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assert h_thesis != h_other
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def test_question_hash_handles_question_with_only_articles_and_topic():
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"""Edge: very short question. `the X` and `X` and `a X` collapse."""
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assert question_hash("the batman") == question_hash("batman")
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assert question_hash("a batman") == question_hash("batman")
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assert question_hash("an batman") == question_hash("batman")
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def test_question_hash_distinct_topics_still_distinct():
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"""Article strip doesn't accidentally collapse different topics."""
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assert question_hash("the batman") != question_hash("the superman")
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assert question_hash("a batman") != question_hash("a superman")
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def test_question_hash_distinct_for_different_text():
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"""Sanity: two genuinely different questions hash to different values."""
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assert question_hash("who is X") != question_hash("who is Y")
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@ -434,6 +434,48 @@ def test_paraphrase_method_label_set_when_any_paraphrase_used():
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assert v["audit_mode"] == "STRICT"
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def test_paraphrase_stopword_filter_does_not_inflate_coverage():
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"""Stopwords (`from`, `with`, `which`, etc.) match almost any English
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text. Excluding them from token-coverage tightens the signal: a span
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where the topical content is missing scores LOWER, not higher.
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The fox 2026-04-29 Batman case: span 'Batman is the alias of Bruce
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Wayne, a wealthy businessman who resides in Gotham City' has
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'wealthy/businessman/resides' missing from the corpus. With a clean
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stopword filter, those topical misses dominate the score and the
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span correctly stays UNGROUNDED rather than scraping over a lowered
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threshold."""
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context = (
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"Batman is the alias of Bruce Wayne. Batman lives in Gotham City."
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)
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answer_with_extra_stopwords = (
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"Batman, who is the alias of Bruce Wayne, lives in Gotham City."
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)
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# Topical tokens (batman, alias, bruce, wayne, lives, gotham, city)
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# all present in source. Stopwords ('which', 'who', etc.) are
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# filtered. Coverage on filtered set = 1.0 → paraphrase verifies.
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v = verify_quotes(answer_with_extra_stopwords, context)
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assert v["audit_mode"] == "STRICT"
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def test_paraphrase_rejects_when_topical_tokens_missing():
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"""Confirms that lowering the bar wouldn't be a quick win — Q1's
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Batman case (missing 'wealthy/businessman/resides') stays
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UNGROUNDED because the topical content isn't in source. Stopword
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filter doesn't rescue it."""
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context = "Batman is Bruce Wayne. Batman fights crime in Gotham."
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answer = (
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"Batman is the alias of Bruce Wayne, a wealthy businessman who "
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"resides in Gotham City."
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)
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# 'wealthy', 'businessman', 'resides', 'alias' all absent from
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# context. Topical content missing — coverage drops below 0.85.
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v = verify_quotes(answer, context)
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# Either UNGROUNDED (substring fail) or HYBRID (some entities) —
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# the key constraint is NOT STRICT (would mean fabrication promoted).
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assert v["audit_mode"] != "STRICT"
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def test_paraphrase_strategy_keeps_quote_strategy_strict():
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"""Quote strategy stays verbatim-only — quotes ARE quotes, paraphrase-
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in-quotes is the model's mistake. Paraphrase fallback applies to span
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