docs(10_Wiki): 위키 전체 재구성 — Topic_* 폴더를 4개 카테고리로 통합 + 대규모 중복 제거

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+---
+id: wiki-2026-0508-hallucination-in-llms
+title: Hallucination in LLMs
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [LLM hallucination, fabrication, confabulation, TruthfulQA, source attribution]
+duplicate_of: none
+source_trust_level: A
+confidence_score: 0.97
+verification_status: applied
+tags: [llm, hallucination, truthfulness, rag, calibration, fact-check]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack:
+  language: Python
+  framework: LangChain / Anthropic / RAG
+---
+
+# Hallucination in LLMs
+
+## 매 한 줄
+> **"매 LLM 의 의 의 false but plausible-sounding output 의 generate"**. 매 modern LLM 의 critical issue. 매 cause: 매 training data, 매 distribution shift, 매 confident next-token. 매 mitigation: RAG, 매 source attribution, 매 calibration, 매 LLM-as-judge.
+
+## 매 핵심
+
+### 매 type
+- **Intrinsic**: 매 input 와 contradict.
+- **Extrinsic**: 매 input 의 의 의 의 verify X.
+- **Factual**: 매 world fact 의 wrong.
+- **Reasoning**: 매 chain 의 fault.
+
+### 매 cause
+- 매 training data 의 imperfect.
+- 매 distribution shift (OOD).
+- 매 next-token objective 의 confidence 의 of unrelated.
+- 매 prompt ambiguity.
+- 매 long-tail rare facts.
+
+### 매 mitigation
+1. **RAG**: 매 ground in source.
+2. **Source attribution**: 매 cite.
+3. **Self-consistency**: 매 multiple sample 의 agree?
+4. **Calibration**: 매 confidence ≈ accuracy.
+5. **LLM-as-judge**: 매 evaluate.
+6. **Fact-checking**: 매 external verify.
+7. **Constrained decoding**: 매 schema enforce.
+8. **Fine-tune** for honesty.
+
+### 매 응용
+1. **Production chatbot**: 매 critical.
+2. **Medical / legal AI**.
+3. **Search / Q&A**.
+4. **Code generation**: 매 API hallucination.
+5. **Summarization**: 매 fabricate.
+
+## 💻 패턴
+
+### Detect (entailment-based)
+```python
+def hallucination_check(claim, source, llm):
+    prompt = f"""Does the source ENTAIL, CONTRADICT, or NEITHER the claim?
+
+Source: {source}
+Claim: {claim}
+
+Output: ENTAIL | CONTRADICT | NEITHER"""
+    return llm.generate(prompt)
+```
+
+### RAG (grounded gen)
+```python
+def rag_answer(question, retriever, llm):
+    docs = retriever.retrieve(question, k=5)
+    context = '\n'.join(f'[{i}] {d.text}' for i, d in enumerate(docs))
+    prompt = f"""Answer based ONLY on the context. Cite [N] for each claim.
+If the context does not contain the answer, say "I don't know."
+
+Context:
+{context}
+
+Question: {question}"""
+    return llm.generate(prompt), docs
+```
+
+### Self-consistency
+```python
+from collections import Counter
+def self_consistency(question, llm, n=10):
+    answers = [llm.generate(question, temperature=0.7) for _ in range(n)]
+    return Counter(answers).most_common(1)[0][0]
+```
+
+### Calibration check
+```python
+def calibration_check(llm, test_questions, threshold=0.05):
+    """매 confidence ≈ accuracy?"""
+    binned_correct = {i: [] for i in range(10)}
+    for q in test_questions:
+        response, conf = llm.generate_with_confidence(q['question'])
+        bin_i = int(conf * 10)
+        binned_correct[bin_i].append(response == q['answer'])
+    
+    ece = 0
+    for bin_i, results in binned_correct.items():
+        if not results: continue
+        bin_acc = np.mean(results)
+        bin_conf = (bin_i + 0.5) / 10
+        ece += abs(bin_acc - bin_conf) * len(results) / len(test_questions)
+    return ece
+```
+
+### LLM-as-judge
+```python
+def judge_truthfulness(claim, judge_llm):
+    prompt = f"""Evaluate the claim for truthfulness.
+
+Claim: "{claim}"
+
+Output JSON:
+- truthful: bool
+- confidence: 0-1
+- evidence: ...
+- if false: corrected version"""
+    return json.loads(judge_llm.generate(prompt))
+```
+
+### TruthfulQA-style eval
+```python
+def truthful_eval(model, questions):
+    correct = 0
+    for q in questions:
+        pred = model.generate(q['question'])
+        # 매 multi-choice or judge match
+        if any(g.lower() in pred.lower() for g in q['gold_answers']):
+            correct += 1
+    return correct / len(questions)
+```
+
+### Token entropy (uncertainty signal)
+```python
+import torch
+def token_entropies(model, prompt, response):
+    inputs = tokenizer(prompt + response, return_tensors='pt')
+    with torch.no_grad():
+        logits = model(**inputs).logits[0]
+    probs = logits.softmax(-1)
+    entropies = -(probs * probs.clamp(min=1e-10).log()).sum(-1)
+    return entropies  # 매 high entropy = uncertain → potential hallucination
+```
+
+### Constrained decoding (schema)
+```python
+from outlines import models, generate
+m = models.transformers('gpt2')
+gen = generate.json(m, MyResponseSchema)
+result = gen('Question: ...')  # 매 must conform
+```
+
+### Fact-check pipeline
+```python
+def fact_check_pipeline(response, llm, fact_checker):
+    claims = extract_claims(response, llm)
+    results = []
+    for claim in claims:
+        evidence = fact_checker.search(claim)
+        verdict = entailment_check(claim, evidence)
+        results.append({'claim': claim, 'verdict': verdict})
+    return results
+```
+
+### Refusal of unknown
+```python
+HONEST_SYSTEM = """You are a helpful assistant. If you don't know an answer with high confidence, say "I don't know" or "I'm not sure" rather than guessing.
+
+Always cite sources when making factual claims."""
+```
+
+### Chain-of-Verification (CoVe)
+```python
+def chain_of_verification(question, llm):
+    # 매 1. Initial answer
+    initial = llm.generate(f'Answer: {question}')
+    
+    # 매 2. Plan verification questions
+    verify_qs = llm.generate(f'List verification questions for: {initial}').split('\n')
+    
+    # 매 3. Answer each independently
+    verifications = [llm.generate(f'Answer: {q}') for q in verify_qs]
+    
+    # 매 4. Refine
+    return llm.generate(f"""Original answer: {initial}
+Verification:
+{format(verify_qs, verifications)}
+
+Refined answer:""")
+```
+
+### LLM honesty fine-tuning (DPO-style)
+```python
+# 매 dataset of (prompt, honest_response, hallucinated_response)
+# 매 DPO trains to prefer honest
+def hallucination_dpo_data(samples):
+    return [{'prompt': s.prompt, 'chosen': s.honest, 'rejected': s.hallucinated} for s in samples]
+```
+
+### Tool-augmented (search)
+```python
+def augmented_answer(question, llm):
+    # 매 LLM decides if external search needed
+    needs_search = llm.classify(question, ['needs_external_data', 'common_knowledge'])
+    if needs_search == 'needs_external_data':
+        results = web_search(question)
+        return rag_answer(question, results, llm)
+    return llm.generate(question)
+```
+
+### Hallucination metric (FActScore)
+```python
+def fact_score(generated_text, llm):
+    """매 atomic facts → check each."""
+    facts = extract_atomic_facts(generated_text, llm)
+    supported = sum(1 for f in facts if check_fact(f) == 'supported')
+    return supported / len(facts)
+```
+
+## 매 결정 기준
+| 상황 | Approach |
+|---|---|
+| Factual Q&A | RAG + citation |
+| Open-ended | Self-consistency + judge |
+| High-stakes | + fact-check + CoVe |
+| Code | Execute + verify |
+| Structured | Constrained decoding |
+| Production | + monitor + abstain |
+
+**기본값**: 매 RAG + 매 source attribution + 매 self-consistency for high-stakes + 매 abstention threshold + 매 LLM-judge eval.
+
+## 🔗 Graph
+- 부모: [[Foundation-Models]]
+- 응용: [[RAG]] · [[AI_Safety_and_Alignment|Constitutional-AI]] · [[TruthfulQA]]
+- Adjacent: [[Epistemology]] · [[Excessive Agency]]
+
+## 🤖 LLM 활용
+**언제**: 매 모든 LLM production. 매 fact-critical.
+**언제 X**: 매 explicitly creative (prefer hallucination).
+
+## ❌ 안티패턴
+- **No grounding**: 매 ungrounded confidently false.
+- **High temp + factual**: 매 noise.
+- **No abstention**: 매 always-answer.
+- **No citation**: 매 unverifiable.
+- **Single sample factual**: 매 lottery.
+
+## 🧪 검증 / 중복
+- Verified (TruthfulQA 2022, FActScore 2023, CoVe 2023, RAG literature).
+- 신뢰도 A.
+
+## 🕓 Changelog
+| 날짜 | 변경 |
+|---|---|
+| 2026-04-26 | Auto |
+| 2026-05-08 | Phase 1 |
+| 2026-05-10 | Manual cleanup — types + 매 RAG / SC / CoVe / FActScore / fact-check code |