---
title: "The Unlocking Spell on Base LLMs — Lin 2023"
description: "Alignment tuning through SFT and RLHF is usually seen as the step that turns a pretrained base LLM into a usable assistant."
authors: ["Bill Yuchen Lin", "Abhilasha Ravichander", "Ximing Lu", "Nouha Dziri", "Melanie Sclar", "Khyathi Chandu", "Chandra Bhagavatula", "Yejin Choi"]
year: 2023
venue: "arXiv:2312.01552"
source: "https://arxiv.org/abs/2312.01552"
doi: "10.48550/arxiv.2312.01552"
arxiv: "2312.01552"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/lin-2023-unlocking-spell-base-llms-rethinking/"
---
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# The Unlocking Spell on Base LLMs: Rethinking Alignment via In-Context Learning

**Authors:** [Bill Yuchen Lin](https://md.research.elamur.ai/free/en/authors/bill-yuchen-lin/), [Abhilasha Ravichander](https://md.research.elamur.ai/free/en/authors/abhilasha-ravichander/), [Ximing Lu](https://md.research.elamur.ai/free/en/authors/ximing-lu/), [Nouha Dziri](https://md.research.elamur.ai/free/en/authors/nouha-dziri/), [Melanie Sclar](https://md.research.elamur.ai/free/en/authors/melanie-sclar/), Khyathi Chandu, [Chandra Bhagavatula](https://md.research.elamur.ai/free/en/authors/chandra-bhagavatula/), [Yejin Choi](https://md.research.elamur.ai/free/en/authors/yejin-choi/) · **Year:** 2023 · **Venue:** arXiv:2312.01552 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2312.01552) · [PDF](https://arxiv.org/pdf/2312.01552) · [DOI 10.48550/arxiv.2312.01552](https://doi.org/10.48550/arxiv.2312.01552) · [arXiv 2312.01552](https://arxiv.org/abs/2312.01552) · [OpenAlex](https://openalex.org/W4389363883)

**Citations:** 10 citations (openalex-snapshot · 2026-10-14) · [129](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Alignment tuning through SFT and RLHF is usually seen as the step that turns a pretrained base LLM into a usable assistant. LIMA suggested this step may change surprisingly little, and the authors test that idea directly. They have an aligned model (e.g., Llama-2-chat, Vicuna-7b, Mistral-7b-Instruct) generate an answer greedily. They then give each prefix to the matching base model and check how it ranks the token the aligned model chose. The two models rarely disagree, and the disagreements cluster on stylistic tokens such as greetings, discourse markers, refusals and safety caveats. Knowledge-heavy tokens mostly keep the same top rank. This implies that the knowledge comes from pretraining, and tuning mainly teaches an assistant's tone. Based on this, the authors propose URIAL, which aligns an untuned model purely through in-context learning: a system prompt plus a few fixed, carefully styled example answers. On the new just-eval-instruct benchmark, URIAL applied to base Mistral-7b and Llama-2-70b matches or beats their SFT and RLHF versions. This raises doubts about how much costly fine-tuning is really needed.

## Key points
- **Token distribution shift analysis:** on average, 77.7% of the aligned model's output tokens are also the base model's top-1 choice, and 92.2% are within its top 3. Shifts are larger at early positions in the response.
- **URIAL (Untuned LLMs with Restyled In-context ALignment):** needs no weight updates. It uses only a system prompt and as few as K=3 constant in-context examples. Each example affirms the query, gives structured detailed content and ends with a summary that includes safety notes.
- **just-eval-instruct:** 1,000 instructions taken from 9 existing datasets (including AlpacaEval, MT-bench and LIMA). GPT-4 scores answers on six aspects: helpfulness, clarity, factuality, depth, engagement and safety. The last 200 instructions are safety tests.
- **Human pairwise evaluation (100 samples):** URIAL on Mistral-7b won 20.0% of comparisons against Mistral-7b-Instruct's 8.9%. URIAL on Llama-2-70b won 16.5% against Llama-2-70b-chat's 8.4%. Most of the remaining comparisons were ties.
- **Implication:** these results support the "superficial alignment hypothesis". Tuning-free, inference-time alignment could replace SFT/RLHF in some settings.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/) · [AI & machine learning](https://md.research.elamur.ai/free/en/shelves/ai/)

## Cited by

Cited by

129 papers of our library cite it (of the 140673 whose bibliographies we read); 5 of them have a page here, 124 not yet.

- [Many-Shot In-Context Learning](https://md.research.elamur.ai/free/en/papers/agarwal-2024-many-shot-context-learning/)
- [Search-o1: Agentic Search-Enhanced Large Reasoning Models](https://md.research.elamur.ai/free/en/papers/li-2025-search-o1-agentic-search-enhanced/)
- [LESS: Selecting Influential Data for Targeted Instruction Tuning](https://md.research.elamur.ai/free/en/papers/xia-2024-less-selecting-influential-data-targeted/)
- [Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing](https://md.research.elamur.ai/free/en/papers/xu-2024-magpie-alignment-data-synthesis-scratch/)
- [Instruction Tuning for Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/zhang-2023-instruction-tuning-large-language-models/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{lin2023unlocking,
  title = {{The Unlocking Spell on Base LLMs: Rethinking Alignment via In-Context Learning}},
  author = {Lin, Bill Yuchen and Ravichander, Abhilasha and Lu, Ximing and Dziri, Nouha and Sclar, Melanie and Chandu, Khyathi and Bhagavatula, Chandra and Choi, Yejin},
  year = {2023},
  doi = {10.48550/arxiv.2312.01552},
  eprint = {2312.01552},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2312.01552}
}
```

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