Beemo (Benchmark of expert-edited machine-generated outputs) is a benchmark for fine-grained machine-generated text detection, which consists of 6.5k texts written by humans, generated by ten open-source instruction-finetuned LLMs and edited by expert annotators for various use cases. Furthermore, each machine-generated text is edited by two state-of-the-art LLMs using several diverse editing prompts, which results in 13.1k machine-generated & LLM-edited texts. We make one of the first attempts to address more practical machine-generated text detection scenarios, where the user refines the LLM output or utilizes another LLM to make it more human-like.
Beemo is available in the HuggingFace datasets library and in this GitHub repository. Please refer to our paper for details on our benchmark creation approach, general statistics, and empirical evaluation results.
Our benchmark is named after BMO (abbreviated from "Be MOre", phonetically spelled "Beemo"), one of the main characters of Adventure Time.
- 📊 Curated by: Toloka, Penn State University, MIT Lincoln Laboratory, and University of Oslo.
- 🌐 Language(s): English
- 🗞️ Paper: arxiv.org/abs/2411.04032
- 🪪 License: MIT
04.02.2025: Our paper is accepted to NAACL 2025, and we release our human annotation guidelines.07.11.2024: The release of Beemo, which includes adding machine-generated & LLM-edited texts and a preprint on arXiv.17.09.2024: The initial release and evaluation of 11 detectors on Beemo.
The Beemo's creation approach involves:
- (a) 🤖 Machine-generated Text Collection: prompting an instruction-finetuned LLM;
- (b) 👩🏻🔬 Expert-based Editing: editing the LLM's output by an expert annotator;
- (c) 🦾 LLM-based Editing: editing the LLM's output by two state-of-the-art LLMs.
🤖 Machine-generated Text Collection
The No Robots 🙅♂️🤖 dataset is used as the source of prompts and corresponding human-written texts across the following categories: Generation, Rewrite, Summarize, Open QA, and Closed QA. We randomly sample each prompt to generate an output with one of ten open-source instruction-finetuned LLMs using the default 🤗 HuggingFace chat templates and inference hyperparameters.
| Name | Base | SFT corpus | License | Paper |
|---|---|---|---|---|
| HuggingFaceH4/zephyr-7b-beta | Mistral-7B-v0.1 | UltraChat, UltradFeedback | MIT | Tunstall et al. (2023) |
| allenai/tulu-2-7b | Llama 2 7B | human-written and synthetic | AI2 ImpACT | Ivison et al (2023) |
| allenai/tulu-2-13b | Llama 2 13B | human-written and synthetic | AI2 ImpACT | Ivison et al. (2023) |
| google/gemma-2b-it | Gemma 2B | human-written and synthetic | Gemma license | Gemma Team et al. (2024) |
| google/gemma-7b-it | Gemma 7B | human-written and synthetic | Gemma license | Gemma Team et al. (2024) |
| meta-llama/Llama-2-7b-chat-hf | Llama 2 7B | Misc. | Llama license | Touvron et al. (2023) |
| meta-llama/Llama-2-13b-chat-hf | Llama 2 13B | Misc. | Llama license | Touvron et al. (2023) |
| meta-llama/Llama-2-70b-chat-hf | Llama 2 70B | Misc. | Llama license | Touvron et al. (2023) |
| mistralai/Mistral-7B-Instruct-v0.1 | Mistral-7B-v0.1 | Misc. | Apache-2.0 | Jiang et. al (2023) |
| mistralai/Mixtral-8x7B-Instruct-v0.1 | Mixtral 8x7B | Misc. | Apache-2.0 | Jiang et al. (2024) |
| meta-llama/Llama-3.1-70B-Instruct | Llama-3.1 | Misc. | Llama | Dubey et al. (2024) |
| GPT-4o | GPT-4 | Misc. | OpenAI | OpenAI (2024) |
Table 1: Overview of the instruction-finetuned LLMs used to create Beemo. GPT-4o and meta-llama/Llama-3.1-70B-Instruct are used only for LLM-based editing. |
👩🏻🔬 Expert-based Editing
The machine-generated texts are edited by an in-house team of annotators, who are well experienced in refining content produced by LLMs.
🦾 LLM-based Editing
The machine-generated texts are "humanized" by GPT-4o and meta-llama/Llama-3.1-70B-Instruct using three editing prompts.
P1:You are given a prompt and a text generated by AI using this prompt. Your task is to edit the AI-generated text to make it sound human-like and error-free. Ensure your overall edits do not exceed 40% of the generated text and the edited text follows the user request. Output only the edited text and do not explain your edits.\n\nPrompt: {prompt}\n\nAI text: {model_output}P2:You are given a pair containing two components: (1) a user prompt for an AI assistant and (2) the AI assistant’s response. Refine the AI-generated response to make it sound more natural. Vary your editing patterns and the portions of text you choose to modify, and ensure your overall edits are 20-40% of the words in the response.\n\nUser prompt: {prompt}\n\nAI-generated response: {model_output}P3:Modify a machine-generated response to a given prompt to make it appear more like it was written by a native English speaker. Ensure the revised version follows the user's intent. You should just give me the revised version without any other words.\n\nPrompt: {prompt}\n\nMachine-generated response: {model_output}
- The prompts and human-written texts from No Robots 🙅♂️🤖 are under the original dataset's license: CC-BY-NC-4.0.
- The machine-generated texts and their LLM-edited versions are subject to the underlying instruction-finetuned LLMs' licensing terms mentioned in Table 1.
- The expert-edited machine-generated texts are available under the MIT license, unless otherwise specified in the underlying instruction-finetuned LLMs' licensing terms.
@article{artemova2024beemo,
title={Beemo: Benchmark of Expert-edited Machine-generated Outputs},
author={Artemova, Ekaterina and Lucas, Jason and Venkatraman, Saranya and Lee, Jooyoung and Tilga, Sergei and Uchendu, Adaku and Mikhailov, Vladislav},
journal={arXiv preprint arXiv:2411.04032},
year={2024}
}
- Vladislav Mikhailov (vladism@ifi.uio.no)
- Ekaterina Artemova (katya-art@toloka.ai)