--- title: "How Far Can Camels Go? Exploring the State of — Wang 2023" description: "Open instruction-tuned models are often promoted as rivals to ChatGPT." authors: ["Yizhong Wang", "Hamish Ivison", "Pradeep Dasigi", "Jack Hessel", "Tushar Khot", "Khyathi Raghavi Chandu", "David Wadden", "Kelsey MacMillan", "Noah A. Smith", "Iz Beltagy", "Hannaneh Hajishirzi"] year: 2023 venue: "arXiv:2306.04751" source: "https://arxiv.org/abs/2306.04751" doi: "10.48550/arxiv.2306.04751" arxiv: "2306.04751" 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/wang-2023-far-camels-go-exploring-state/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/wang-2023-far-camels-go-exploring-state/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2023](https://md.research.elamur.ai/free/en/papers/2023/page/4/) / How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources # How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources **Authors:** [Yizhong Wang](https://md.research.elamur.ai/free/en/authors/yizhong-wang/), [Hamish Ivison](https://md.research.elamur.ai/free/en/authors/hamish-ivison/), [Pradeep Dasigi](https://md.research.elamur.ai/free/en/authors/pradeep-dasigi/), [Jack Hessel](https://md.research.elamur.ai/free/en/authors/jack-hessel/), [Tushar Khot](https://md.research.elamur.ai/free/en/authors/tushar-khot/), Khyathi Raghavi Chandu, [David Wadden](https://md.research.elamur.ai/free/en/authors/david-wadden/), Kelsey MacMillan, [Noah A. Smith](https://md.research.elamur.ai/free/en/authors/noah-a-smith/), [Iz Beltagy](https://md.research.elamur.ai/free/en/authors/iz-beltagy/), [Hannaneh Hajishirzi](https://md.research.elamur.ai/free/en/authors/hannaneh-hajishirzi/) · **Year:** 2023 · **Venue:** arXiv:2306.04751 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/) **Links:** [Publisher's page](https://arxiv.org/abs/2306.04751) · [PDF](https://arxiv.org/pdf/2306.04751) · [DOI 10.48550/arxiv.2306.04751](https://doi.org/10.48550/arxiv.2306.04751) · [arXiv 2306.04751](https://arxiv.org/abs/2306.04751) · [OpenAlex](https://openalex.org/W7133221203) **Citations:** 42 citations (openalex-snapshot · 2026-10-14) · [262](#cited-by) in our library, of the 140673 papers whose bibliographies we read ## Summary Open instruction-tuned models are often promoted as rivals to ChatGPT. Those claims usually rest on narrow tests that mostly use model-based judging. This makes it hard to tell which public instruction datasets are actually useful. The authors ran a controlled comparison. They fine-tuned LLaMA, LLaMA-2, OPT and Pythia base models (6.7B–65B parameters) on 12 open instruction corpora. The corpora range from human-written (Dolly, Open Assistant) to NLP-derived (SuperNI, Flan V2) and model-distilled (Alpaca, ShareGPT, Baize). All data were converted to a single chat format, with loss computed only on assistant replies. Evaluation used benchmarks, GPT-4 judging and human ratings to measure the models' grasp of facts, their reasoning ability, their performance across languages, code generation, safety, and how well they follow free-form instructions. Each dataset tends to boost particular skills, and no single source or mixture wins everywhere. Stronger base models help consistently. The resulting suite, TÜLU, has the best average results but still falls short of ChatGPT. The study matters because it shows that preference-style evaluation can mask capability gaps. It also releases models, data and code for reproducible research. ## Key points - Training covered 12 instruction datasets, with CoT and Flan V2 subsampled to 100K examples. Base models were LLaMA-1 (7B–65B), LLaMA-2 (7B, 13B), OPT 6.7B and Pythia 6.9B. - TÜLU combines 7 open datasets. A separate human-only mixture uses Flan V2, CoT, Dolly and Open Assistant. TÜLU 65B was reportedly the largest fully instruction-tuned public LLaMA variant at release. - On any given evaluation, the top open model reaches on average about 87% of ChatGPT's score and 73% of GPT-4's. - TÜLU stays within 29% of the best model on every individual task while having the best average. - GPT-4-based preference scores correlate strongly with how many unique tokens a model generates. Such judgments can therefore favour long, varied answers over real capability. - All checkpoints, code and the evaluation framework are released at github.com/allenai/open-instruct. *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: [Other](https://md.research.elamur.ai/free/en/shelves/other/) ## Cited by Cited by 262 papers of our library cite it (of the 140673 whose bibliographies we read); 27 of them have a page here, 235 not yet. - [Fanar: An Arabic-Centric Multimodal Generative AI Platform](https://md.research.elamur.ai/free/en/papers/abbas-2025-fanar-arabic-centric-multimodal-generative/) - [Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection](https://md.research.elamur.ai/free/en/papers/asai-2023-self-rag-learning-retrieve-generate/) - [Qwen Technical Report](https://md.research.elamur.ai/free/en/papers/bai-2023-qwen-technical-report/) - [LongAlign: A Recipe for Long Context Alignment of Large Language Models](https://md.research.elamur.ai/free/en/papers/bai-2024-longalign-recipe-long-context/) - [Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models](https://md.research.elamur.ai/free/en/papers/deitke-2024-molmo-pixmo-open-weights-open/) - [How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition](https://md.research.elamur.ai/free/en/papers/dong-2024-how-abilities-large-language-models/) - [Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2](https://md.research.elamur.ai/free/en/papers/ivison-2023-camels-changing-climate-enhancing-lm/) - [Tulu 3: Pushing Frontiers in Open Language Model Post-Training](https://md.research.elamur.ai/free/en/papers/lambert-2024-tulu-3-pushing-frontiers-open/) - [The Unlocking Spell on Base LLMs: Rethinking Alignment via In-Context Learning](https://md.research.elamur.ai/free/en/papers/lin-2023-unlocking-spell-base-llms-rethinking/) - [Generative Representational Instruction Tuning](https://md.research.elamur.ai/free/en/papers/muennighoff-2024-generative-representational-instruction-tuning/) - [Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!](https://md.research.elamur.ai/free/en/papers/qi-2023-fine-tuning-aligned-language-models/) - [Large Language Model Alignment: A Survey](https://md.research.elamur.ai/free/en/papers/shen-2023-large-language-model-alignment-survey/) - [A Roadmap to Pluralistic Alignment](https://md.research.elamur.ai/free/en/papers/sorensen-2024-roadmap-pluralistic-alignment/) - [Aligning Large Multimodal Models with Factually Augmented RLHF](https://md.research.elamur.ai/free/en/papers/sun-2023-aligning-large-multimodal-models-factually/) - [Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model](https://md.research.elamur.ai/free/en/papers/ustun-2024-aya-model-instruction-finetuned-open/) - [Aligning Large Language Models with Human: A Survey](https://md.research.elamur.ai/free/en/papers/wang-2023-aligning-large-language-models-human/) - [MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning](https://md.research.elamur.ai/free/en/papers/wang-2023-mathcoder-seamless-code-integration-llms/) - [LESS: Selecting Influential Data for Targeted Instruction Tuning](https://md.research.elamur.ai/free/en/papers/xia-2024-less-selecting-influential-data-targeted/) - [WizardLM: Empowering large pre-trained language models to follow complex instructions](https://md.research.elamur.ai/free/en/papers/xu-2023-wizardlm-empowering-large-pre-trained/) - [Rethinking Benchmark and Contamination for Language Models with Rephrased Samples](https://md.research.elamur.ai/free/en/papers/yang-2023-rethinking-benchmark-contamination-language-models/) - [MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning](https://md.research.elamur.ai/free/en/papers/yue-2023-mammoth-building-math-generalist-models/) - [Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?](https://md.research.elamur.ai/free/en/papers/yue-2025-reinforcement-learning-really-incentivize-reasoning/) - [AgentTuning: Enabling Generalized Agent Abilities for LLMs](https://md.research.elamur.ai/free/en/papers/zeng-2023-agenttuning-enabling-generalized-agent/) - [Instruction Tuning for Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/zhang-2023-instruction-tuning-large-language-models/) - [Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2023-siren-s-song-ai-ocean/) - [A Survey of Large Language Models](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/) - [LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models](https://md.research.elamur.ai/free/en/papers/zheng-2024-llamafactory-unified-efficient-fine-tuning/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @misc{wang2023how, title = {{How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources}}, author = {Wang, Yizhong and Ivison, Hamish and Dasigi, Pradeep and Hessel, Jack and Khot, Tushar and Chandu, Khyathi Raghavi and Wadden, David and MacMillan, Kelsey and Smith, Noah A. and Beltagy, Iz and Hajishirzi, Hannaneh}, year = {2023}, doi = {10.48550/arxiv.2306.04751}, eprint = {2306.04751}, archivePrefix = {arXiv}, url = {https://doi.org/10.48550/arxiv.2306.04751} } ``` - [← previous How does GPT-2 compute greater-than?: Interpreting mathematical abilities in a pre-trained language model](https://md.research.elamur.ai/free/en/papers/hanna-2023-gpt-2-compute-greater-than/) - [next → How Language Model Hallucinations Can Snowball](https://md.research.elamur.ai/free/en/papers/zhang-2023-language-model-hallucinations-snowball/) - [↑ back to papers of 2023](https://md.research.elamur.ai/free/en/papers/2023/page/4/) ## References 56 references, 22 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. 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