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

---
id: wiki-2026-0508-conversational-maxims
title: Conversational Maxims (Grice)
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Gricean Maxims, Cooperative Principle, Grice's Maxims]
duplicate_of: none
source_trust_level: A
confidence_score: 0.95
verification_status: applied
tags: [linguistics, pragmatics, nlp, dialogue, prompt-engineering]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: N/A (theory)
  framework: 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
```text
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
```python
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)
```python
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
```python
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
```text
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
```text
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]]
- 응용: [[Prompt Engineering]]
- Adjacent: [[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 |