Antigravity Agent
156 lines
5.2 KiB
Markdown
156 lines
5.2 KiB
Markdown
---
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id: wiki-2026-0508-introspection-자기성찰
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title: Introspection (자기성찰)
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Self-Reflection, 자기성찰, LLM Introspection]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [llm, prompting, self-reflection, philosophy-of-mind, agent]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: python
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framework: langchain
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---
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# Introspection (자기성찰)
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## 매 한 줄
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> **"매 모델이 자기 출력을 다시 읽고 평가/수정"**. 철학에서는 1인칭 자기 의식 접근을 의미하고, LLM에서는 self-critique → revise loop로 정확도/정합성을 끌어올리는 핵심 prompting pattern.
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## 매 핵심
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### 매 두 가지 의미
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- **철학 (Locke, Kant)**: mind 자체를 관찰하는 1인칭 access. qualia, self-model.
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- **LLM**: 자기 응답을 input 으로 다시 받아 비판/개선. **Reflexion**, **Self-Refine**, **CoVe** 계열.
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### 매 동작 원리
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1. **Generate**: 초안 응답 produce.
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2. **Critique**: 같은 모델이 초안을 평가 (오류, 누락, 가정).
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3. **Revise**: critique 반영 재생성.
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4. (선택) 수렴할 때까지 반복.
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### 매 응용
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1. Reasoning 정확도 향상 (math, code).
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2. Hallucination 검출 (CoVe — Chain of Verification).
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3. Agent 환경: tool 호출 결과 self-evaluate 후 재시도.
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4. RLHF reward modeling 대안 (constitutional AI).
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## 💻 패턴
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### Self-Refine (single-model loop)
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```python
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def self_refine(prompt, model, max_iters=3):
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answer = model.invoke(f"Answer: {prompt}")
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for _ in range(max_iters):
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feedback = model.invoke(
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f"Critique this answer (errors, gaps):\n{answer}"
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)
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if "no issues" in feedback.lower():
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break
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answer = model.invoke(
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f"Original: {prompt}\nDraft: {answer}\nFeedback: {feedback}\nRevised:"
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)
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return answer
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```
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### Reflexion (verbal RL)
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```python
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class ReflexionAgent:
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def __init__(self, llm):
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self.llm = llm
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self.memory = [] # past reflections
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def act(self, task):
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ctx = "\n".join(self.memory)
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result = self.llm(f"{ctx}\nTask: {task}")
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if not self.evaluate(result):
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reflection = self.llm(f"Why did this fail?\n{result}")
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self.memory.append(reflection)
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return result
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```
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### Chain of Verification (CoVe)
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```python
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def cove(question, llm):
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draft = llm(f"Q: {question}")
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plan = llm(f"List verification questions for:\n{draft}")
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answers = [llm(q) for q in plan.split("\n")]
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return llm(f"Original:{draft}\nVerifications:{answers}\nFinal:")
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```
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### Constitutional AI (self-critique with principles)
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```python
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PRINCIPLES = ["harmless", "honest", "helpful"]
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def constitutional_revise(response, llm):
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for p in PRINCIPLES:
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critique = llm(f"Critique by '{p}':\n{response}")
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response = llm(f"Revise per critique:\n{critique}")
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return response
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```
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### Confidence-gated introspection
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```python
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def maybe_introspect(answer, llm, threshold=0.7):
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score = float(llm(f"Confidence 0-1 in:\n{answer}").strip())
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if score < threshold:
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return self_refine(answer, llm)
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return answer
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```
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### LangChain self-critique chain
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```python
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from langchain.chains import LLMChain, SequentialChain
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draft = LLMChain(llm=llm, prompt=draft_prompt, output_key="draft")
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critic = LLMChain(llm=llm, prompt=critic_prompt, output_key="critique")
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final = LLMChain(llm=llm, prompt=final_prompt, output_key="final")
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chain = SequentialChain(chains=[draft, critic, final],
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input_variables=["q"],
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output_variables=["final"])
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| 단순 factual Q&A | introspection 불필요 (latency↑) |
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| Math / code | Self-Refine + verifier |
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| Long-form 사실 검증 | CoVe |
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| Agent 실패 학습 | Reflexion (memory 누적) |
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| Safety alignment | Constitutional AI |
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**기본값**: 1-shot Self-Refine (1회 critique → revise). 반복은 비용 대비 효과 체감 빠름.
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## 🔗 Graph
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- 부모: [[Prompt-Engineering]], [[LLM]]
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- 변형: [[Reflexion]], [[Self-Refine]], [[Chain-of-Verification]]
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- 응용: [[Agent-Frameworks]], [[Constitutional-AI]]
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- Adjacent: [[Chain-of-Thought]], [[Tree-of-Thoughts]], [[Philosophy-of-Mind]]
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## 🤖 LLM 활용
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**언제**: multi-step reasoning, 사실 검증 필요한 long-form, agent 실패 분석, alignment.
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**언제 X**: 단답형 classification, latency 민감, cost-bound batch inference.
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## ❌ 안티패턴
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- **Critique 동일 모델만 사용**: confirmation bias — 가능하면 stronger judge 사용.
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- **무한 loop**: 수렴 조건 없이 반복 → 비용 폭증, 답 표류.
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- **Critique 무시**: revise 단계에서 critique 미반영 prompt.
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- **자기성찰 == 자의식**: LLM introspection 은 functional pattern. 철학적 self-awareness 와 구분.
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## 🧪 검증 / 중복
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- Madaan et al. 2023 (Self-Refine), Shinn et al. 2023 (Reflexion), Dhuliawala et al. 2023 (CoVe).
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- Bai et al. 2022 (Constitutional AI, Anthropic).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — 철학/LLM 두 의미 통합, Self-Refine/Reflexion/CoVe/Constitutional 패턴 정리 |
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