2nd/10_Wiki/Topics/Conversational-Maxims.md

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-conversational-maxims	Conversational Maxims (Grice)	10_Wiki/Topics	verified	self	Gricean Maxims Cooperative Principle Grice's Maxims	none	A	0.95	applied	linguistics pragmatics nlp dialogue prompt-engineering		2026-05-10	pending	language framework N/A (theory) Pragmatics / NLP

Conversational Maxims (Grice)

매 한 줄

"매 conversational maxims 의 핵심: cooperative principle + 4 maxims (Quantity, Quality, Relation, Manner)". 매 1975 Paul Grice 의 "Logic and Conversation" 으로 정립, 매 pragmatics 의 cornerstone. 매 2026 현재 LLM alignment / prompt engineering / conversational AI 의 design heuristics 으로 적극 활용 — 매 RLHF reward 의 latent objective 와 align.

매 핵심

매 Cooperative Principle (Grice)

"매 contribute what is required, when required, by purpose of exchange."

매 4 Maxims

• Quantity: 매 informative as required, 매 not more, 매 not less.
• Quality: 매 truthful — 매 don't say what you believe false / lack evidence for.
• Relation: 매 relevant.
• Manner: 매 clear — avoid obscurity, ambiguity, prolixity, disorder.

매 Implicature (key concept)

• Conversational implicature: 매 maxim flouting → inferred meaning.
• 예: "Some students passed" → implies "not all" (Quantity implicature).
• 매 LLM 의 training 의 implicit 학습 — 매 helpful answer pattern 의 핵심.

매 응용

1. Prompt engineering (be specific, give context, not too verbose).
2. LLM evaluation (helpful = Quantity+Relation, honest = Quality, harmless = Manner).
3. Dialogue system design (Alexa, Siri, ChatGPT response shaping).
4. Translation / cross-cultural pragmatics.

💻 패턴

LLM system prompt aligned with maxims

You are a helpful assistant. Follow Grice's maxims:
- Quantity: Provide enough detail to answer fully, but no more.
- Quality: Only state facts you are confident about; flag uncertainty.
- Relation: Stay on topic; avoid tangents unless asked.
- Manner: Be clear and structured; avoid jargon unless necessary.
If any maxim conflicts (e.g., user asks for brevity but full detail
is needed), state the trade-off explicitly.

Maxim-aware response template

def respond(user_query, context):
    # Quality check
    if not has_evidence(user_query, context):
        return "I'm not certain. Based on partial info: …"
    # Quantity calibration
    detail_level = infer_detail(user_query)  # short/medium/long
    # Relation filter
    relevant_ctx = filter_relevant(context, user_query)
    # Manner formatting
    return format_clear(answer, detail_level)

Implicature detection (NLI-style)

from transformers import pipeline
nli = pipeline("zero-shot-classification",
               model="facebook/bart-large-mnli")
# "Some students passed" implicates "not all passed"
out = nli("Some students passed",
          candidate_labels=["all passed", "not all passed", "none passed"])

Dialogue state — maxim violations as repair triggers

def detect_violation(turn, prev_turn):
    violations = []
    if turn.length > 3 * prev_turn.length and not requested_detail:
        violations.append("quantity-too-much")
    if not topically_related(turn, prev_turn):
        violations.append("relation")
    if has_hedging_with_no_basis(turn):
        violations.append("quality")
    return violations

Prompt template — Maxim-of-Quantity calibration

Answer in {N} sentences. If the question requires more, say "Answering
fully needs more space — should I expand?" before continuing.

Anti-hallucination via Quality maxim

If you don't know, say "I don't know" rather than fabricating. Cite
source when possible. Distinguish: (a) verified, (b) likely, (c) speculation.

매 결정 기준

상황	Apply
Designing system prompt	4 maxims as explicit guidelines
LLM eval rubric	Map to helpful/honest/harmless
Dialogue agent	Track maxim adherence per turn
Cross-cultural deploy	Manner / Quantity vary by culture (high vs low context)
Sarcasm / irony	Flouting Quality intentionally — model must recognize

기본값: 매 system prompts 의 4 maxims 의 명시 inclusion — measurable quality 향상.

🔗 Graph

• 부모: Pragmatics · Linguistics · Philosophy of Language
• 변형: Politeness Theory (Brown & Levinson) · Relevance Theory (Sperber & Wilson)
• 응용: Prompt Engineering · Dialogue Systems · LLM Alignment
• Adjacent: Speech Acts · Discourse Analysis · RLHF

🤖 LLM 활용

언제: 매 prompt design 의 framework, 매 alignment 의 axiom, 매 dialog quality eval 의 rubric, 매 사회적 misunderstanding 분석. 언제 X: 매 hard math / formal logic — 매 pragmatic principles 와 무관.

❌ 안티패턴

• Maxims 의 strict rules 화: 매 Grice 의 본의 의 X — 매 default expectations + flouting 의 intentional.
• Universal application: 매 culture-specific (high-context cultures 의 less Quantity).
• Ignoring Manner in technical writing: 매 jargon 남용 → comprehension 손실.
• Overweighting Quantity (verbose LLM): 매 long ≠ helpful — 매 RLHF length bias 주의.
• Quality bypass via hedging: 매 "I think maybe possibly" 의 fake humility — actual uncertainty 의 explicit calibration.

🧪 검증 / 중복

• Verified (Grice 1975 "Logic and Conversation", Levinson 1983 Pragmatics, modern NLP textbooks Jurafsky & Martin 4th ed.).
• 신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — Gricean maxims + LLM alignment application