From e3eb6d63d617479c16bbee548776ca07dbf25f42 Mon Sep 17 00:00:00 2001 From: andy_yang <73746844+andy-yang-1@users.noreply.github.com> Date: Mon, 13 Nov 2023 23:19:48 -0800 Subject: [PATCH] add LLM decontaminator (#38) * add decontaminator * fix small typo * add explanation for cases * add code block * tune writing * rm overview * small typo * slightly change flow * change overview position * captilize title * enlarge overview graph * change date * change first paragraph * add rephraser * add date for mathinstruct * add decontaminator words * add acknowledgement * figure resolution * click bait title * Ion's opinion & resolution * fix small typo * change date * weilin comment * resolution & f1 table & simplify & remove frank & add citation * update figure * fit full & add e.g. * change resolution & add author * change rephrase score fig * fix rephraser --- blog/2023-11-14-llm-decontaminator.md | 130 ++++++++++++++++++ .../blog/decontaminator/MATH-rephrase.png | Bin 0 -> 37736 bytes .../blog/decontaminator/MMLU-f1score.png | Bin 0 -> 60921 bytes .../blog/decontaminator/MMLU-us-f1score.png | Bin 0 -> 215467 bytes .../decontaminator/codealpaca-rephrase.png | Bin 0 -> 191739 bytes .../blog/decontaminator/gsm-8k-rephrase.png | Bin 0 -> 157178 bytes .../blog/decontaminator/llama-Frank.png | Bin 0 -> 129726 bytes .../blog/decontaminator/llama-rephraser.png | Bin 0 -> 335719 bytes .../images/blog/decontaminator/overview.png | Bin 0 -> 369870 bytes .../decontaminator/real-world-rephrase.png | Bin 0 -> 213640 bytes .../rephrase-score_with_border.png | Bin 0 -> 105979 bytes public/images/blog/decontaminator/run-e2e.png | Bin 0 -> 139917 bytes .../decontaminator/starcoder-rephrase.png | Bin 0 -> 254775 bytes 13 files changed, 130 insertions(+) create mode 100644 blog/2023-11-14-llm-decontaminator.md create mode 100644 public/images/blog/decontaminator/MATH-rephrase.png create mode 100644 public/images/blog/decontaminator/MMLU-f1score.png create mode 100644 public/images/blog/decontaminator/MMLU-us-f1score.png create mode 100644 public/images/blog/decontaminator/codealpaca-rephrase.png create mode 100644 public/images/blog/decontaminator/gsm-8k-rephrase.png create mode 100644 public/images/blog/decontaminator/llama-Frank.png create mode 100644 public/images/blog/decontaminator/llama-rephraser.png create mode 100644 public/images/blog/decontaminator/overview.png create mode 100644 public/images/blog/decontaminator/real-world-rephrase.png create mode 100644 public/images/blog/decontaminator/rephrase-score_with_border.png create mode 100644 public/images/blog/decontaminator/run-e2e.png create mode 100644 public/images/blog/decontaminator/starcoder-rephrase.png diff --git a/blog/2023-11-14-llm-decontaminator.md b/blog/2023-11-14-llm-decontaminator.md new file mode 100644 index 00000000..c0957a6d --- /dev/null +++ b/blog/2023-11-14-llm-decontaminator.md @@ -0,0 +1,130 @@ +--- +title: "Cache me if you can! How to beat GPT-4 with a 13B model" +author: "Shuo Yang*, Wei-Lin Chiang*, Lianmin Zheng*, Joseph E. Gonzalez, Ion Stoica" +date: "Nov 14, 2023" +previewImg: /images/blog/decontaminator/rephrase-score_with_border.png +--- + + +Announcing Llama-rephraser: 13B models reaching GPT-4 performance in major benchmarks (MMLU/GSK-8K/HumanEval)! +To ensure result validity, we followed OpenAI's decontamination method and found no evidence of data contamination. + + + + +What's the trick behind it? Well, rephrasing the test set is all you need! We simply paraphrase a test sample or translate it into a different language. It turns out a 13B LLM is smart enough to "generalize" beyond such variations and reaches drastically high benchmark performance. So, did we just make a big breakthrough? Apparently, there is something wrong with our understanding of contamination. + +In this blog post, we point out why contamination is still poorly understood and how existing decontamination measures fail to capture such nuances. To address such risks, we propose a stronger [LLM-based decontaminator](https://github.com/lm-sys/llm-decontaminator) and apply it to real-world training datasets (e.g., the Stack, RedPajama), revealing significant test overlap with widely used benchmarks. +For more technical details, please refer to our [paper](https://arxiv.org/pdf/2311.04850.pdf). + + +## **What's wrong with existing decontamination measures?** + +Contamination occurs when test set information is leaked in the training set, resulting in an overly optimistic estimate of the model’s performance. +Despite being recognized as a crucial issue, understanding and detecting contamination remains an open and challenging problem. + +The most commonly used approaches are n-gram overlap and embedding similarity search. +N-gram overlap relies on string matching to detect contamination, widely used by leading developments such as [GPT-4](https://arxiv.org/pdf/2303.08774.pdf), [PaLM](https://arxiv.org/pdf/2204.02311.pdf), and [Llama-2](https://arxiv.org/pdf/2307.09288.pdf). +Embedding similarity search uses the embeddings of pre-trained models (e.g., BERT) to find similar and potentially contaminated examples. + +However, we show that simple variations of the test data (e.g., paraphrasing, translation) can easily bypass existing simple detection methods. +We refer to such variations of test cases as _Rephrased Samples_. + +Below we demonstrate a rephrased sample from the MMLU benchmark. We show that if such samples are included in the training set, a 13B model can reach drastically high performance (MMLU 85.9). +Unfortunately, existing detection methods (e.g., n-gram overlap, embedding similarity) fail to detect such contamination. The embedding similarity approach struggles to distinguish the rephrased question from other questions in the same subject (high school US history). + + + + + + +With similar rephrasing techniques, we observe consistent results in widely used coding and math benchmarks such as HumanEval and GSM-8K (shown in the cover figure). Therefore, being able to detect such rephrased samples becomes critical. + + + +## **Stronger Detection Method: LLM Decontaminator** + +To address the risk of possible contamination, we propose a new contamination detection method “LLM decontaminator”. + +This LLM decontaminator involves two steps: + + 1. For each test case, LLM decontaminator identifies the top-k training items with the highest similarity using the embedding similarity search. + 2. From these items, LLM decontaminator generates k potential rephrased pairs. Each pair is evaluated for rephrasing using an advanced LLM, such as GPT-4. + +Results show that our proposed LLM method works significantly better than existing methods on removing rephrased samples. + +### **Evaluating Different Detection Methods** + +To compare different detection methods, we use MMLU benchmark to construct 200 prompt pairs using both the original and rephrased test sets. These comprised 100 random pairs and 100 rephrased pairs. +The f1 score on these pairs provides insight into the detection methods' ability to detect contamination, with higher values indicating more precise detection. +As shown in the following table, except for the LLM decontaminator, all other detection methods introduce some false positives. Both rephrased and translated samples successfully evade the n-gram overlap detection. With multi-qa BERT, the embedding similarity search proves ineffective against translated samples. Our proposed LLM decontaminator is more robust in all cases with the highest f1 scores. + + + + + +## **Contamination in Real-World Dataset** + +We apply the LLM decontaminator to widely used real-world datasets (e.g., the Stack, RedPajama, etc) and identify a substantial amount of rephrased samples. The table below displays the contamination percentage of different benchmarks in each training dataset. + + + + +Below we show some detected samples. + +[CodeAlpaca](https://github.com/sahil280114/codealpaca) contains 20K instruction-following synthetic data generated by GPT, which is widely used for instruction fine-tuning (e.g., [Tulu](https://huggingface.co/TheBloke/tulu-30B-fp16)). + +A rephrased example in CodeAlpaca is shown below. + + + +This suggests contamination may subtly present in synthetic data generated by LLMs. In the Phi-1 [report](https://arxiv.org/pdf/2306.11644.pdf), they also discover such semantically similar test samples that are undetectable by n-gram overlap. + + +[MATH](https://github.com/hendrycks/math) is a widely recognized math training dataset that spans various mathematical domains, including algebra, geometry, and number theory. +Surprisingly, we even find contamination between the train-test split in the MATH benchmark as shown below. + + + + +[StarCoder-Data](https://huggingface.co/datasets/bigcode/starcoderdata) is used for training StarCoder and StarCoderBase, and it contains 783GB of code in 86 programming languages. In the StarCoder [paper](https://arxiv.org/pdf/2305.06161.pdf), the code training data was decontaminated by removing files that contained docstrings or solutions from HumanEval. However, there are still some samples detected by LLM decontaminator. + + + +## **Use LLM Decontaminator to Scan Your Data** + +Based on the above study, we suggest the community adopt a stronger decontamination method when using any public benchmarks. Our proposed LLM decontaminator is open-sourced on GitHub. +Here we show how to remove rephrased samples from training data using the LLM decontaminator tool. The following example can be found [here](https://github.com/lm-sys/llm-decontaminator#detect). + +[Pre-process](https://github.com/lm-sys/llm-decontaminator#pre-process) training data and test data. +The LLM decontaminator accepts the dataset in jsonl format, with each line corresponding to a `{"text": data}` entry. + +Run [End2End](https://github.com/lm-sys/llm-decontaminator#end2end) detection. +The following command builds a top-k similar database based on sentence bert and uses GPT-4 to check one by one if they are rephrased samples. You can select your embedding model and detection model by modifying the parameters. + + + + +## **Conclusion** + +In this blog, we show that contamination is still poorly understood. With our proposed decontamination method, we reveal significant previously unknown test overlap in real-world datasets. We encourage the community to rethink benchmark and contamination in LLM context, and adopt stronger decontamination tools when evaluating LLMs on public benchmarks. + + +## **Acknowledgment** + +We would like to express our gratitude to Ying Sheng for the early discussion on rephrased samples. +We also extend our thanks to Dacheng Li, Erran Li, Hao Liu, Jacob Steinhardt, Hao Zhang, and Siyuan Zhuang for providing insightful feedback. + + +## **Citation** + +``` +@misc{yang2023rethinking, + title={Rethinking Benchmark and Contamination for Language Models with Rephrased Samples}, + author={Shuo Yang and Wei-Lin Chiang and Lianmin Zheng and Joseph E. Gonzalez and Ion Stoica}, + year={2023}, + eprint={2311.04850}, + archivePrefix={arXiv}, + primaryClass={cs.CL} +} +``` \ No newline at end of file diff --git a/public/images/blog/decontaminator/MATH-rephrase.png b/public/images/blog/decontaminator/MATH-rephrase.png new file mode 100644 index 0000000000000000000000000000000000000000..3d0ef6975b45bed4aa52e95a33f9c929766a5887 GIT binary patch literal 37736 zcmdRVcU05Q*Ctj#rHBFw0@6E3m#$Pp@4blh5`6etvh(+QLyKkW9H02mOVP+n*UiogWaVK?pzG@BZsqA^ zYfe3~L_qMI0HE+%&o^U#$u{t@GDFA7Tx)X!?QII4hTELCIS%O;Gij!_CzB^`=F8pL z%^#bf;ygSgU*v7Lt@=sfTPV5ap=&nvLni7Cxi_bnwiWq913UYzL%VB9CeBDEeAD>P zTPY7K_J99=Z(v;c+gW^~&F-Qb4J~cJ+2-(v4<8yDUbb3Tx;y@x>Fd{5TprG)Y zjqKlIMOBrR7sp*>rnOE<7reJ_P%CO*Ut^AvxDDIB)!g`}%wsDo2F)Pmn!y5av>bZk zuKn0{Btsl(Q08Chc8y)3Gj@sWy`ThI%Q9I6}0!FP=^6$%$FIp{RgDw%F zp`m%Q*>}wFl5ZUyS-vU$ckDRkZ{EC#I$VMvaQN4edoXGp|NnY1#Cvasl~*(InfuAv zS+mc9Ne&zFKUv=$n%}SWOhXM7ojNv#(uBg&>HkUpu-XBG8Ri}Q(JA`!uh<-<@!{kT 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