BUST : Benchmark for the evaluation of detectors of LLM -Generated Text (2024)

Abstract: We introduce BUST, a comprehensive benchmark designed to evaluate detectors of texts generated by instruction-tuned large language models (LLMs). Unlike previous benchmarks, our focus lies on evaluating the performance of detector systems, acknowledging the inevitable influence of the underlying tasks and different LLM generators. Our benchmark dataset consists of 25K texts from humans and 7 LLMs responding to instructions across 10 tasks from 3 diverse sources. Using the benchmark, we evaluated 5 detectors and found substantial performance variance across tasks. A meta-analysis of the dataset characteristics was conducted to guide the examination of detector performance. The dataset was analyzed using diverse metrics assessing linguistic features like fluency and coherence, readability scores, and writer attitudes, such as emotions, convincingness, and persuasiveness. Features impacting detector performance were investigated with surrogate models, revealing emotional content in texts enhanced some detectors, yet the most effective detector demonstrated consistent performance, irrespective of writer’s attitudes and text styles. Our approach focused on investigating relationships between the detectors’ performance and two key factors: text characteristics and LLM generators. We believe BUST will provide valuable insights into selecting detectors tailored to specific text styles and tasks and facilitate a more practical and in-depth investigation of detection systems for LLM-generated text.

## introduction in recent years, the development of large language models (llms) has surged, with notable releases such as gpt-4, llama-2 and falcon. as these models have advanced, distinguishing between texts written by humans and texts generated by llms context: belize, formerly known as british honduras, is a caribbean country located on the northeastern coast of central america. belize is bordered on the northwest by mexico, on the east by the caribbean sea, and on the south and west by guatemala. it has an area of 22,970 square kilometres (8,867 sq mi) and a population of @xcite . its mainland is about 290 km (180 mi) long and 110 km (68 mi) wide. it has the lowest population and population density in central america. ## related work following, we present related work on generated text detectors and datasets for mgt. generated text detectors mgt detection has evolved significantly over the years. initial efforts focused on detecting differences in linguistic features and using statistical measures such as perplexity, relative entropy, and style similarities @xcite . @xcite provided a suite of baseline statistical methods known as "gltr" to help humans detect mgt without prior training. recent developments like detect-gpt @xcite define a curvature-based criterion using log-likelihoods computed by the model in question, circumventing the need to train a separate classifier, collect a dataset of real or generated passages, or explicitly watermark the generated text. similarly, @xcite presented detectllm, a zero-shot approach detecting a range of llms using log-rank information. another prevalent strategy in this area involves the detection of mgt as a classification task. in this context, various pre-trained language models such as vicuna @xcite or roberta @xcite are fine-tuned on datasets that combine human-written with their corresponding mgt examples. notably, many of these models are specialized for specific tasks, as grover @xcite . others are tailored to specific domains @xcite or aim to detect output from specific generator models @xcite . llm generated text datasets the datasets used to evaluate systems capable of distinguishing between human-written and mgt vary considerably. some developers of detector systems have chosen to create their own datasets for this purpose @xcite @xcite . however, publicly available datasets have often been created for very specific purposes. in particular, some are designed for specific generator models @xcite . other datasets are limited to a single task @xcite , while some are specific to a particular domain, such as the cheat dataset @xcite , which solely focuses on generating academic abstracts. the turingbench dataset @xcite ) provides a comprehensive benchmark containing news articles generated by 19 different models, based on their titles, and juxtaposed with the corresponding human-authored articles. however, the text-generating capabilities of the models in this dataset are not on par with the advanced capabilities of current llms. furthermore, @xcite presented a dataset aimed at investigating the human ability to distinguish between texts written by humans and machines. notably, this dataset spans multiple domains and incorporates various models, though it is limited by having only 50 samples for each generator. more recently, datasets encompassing a wider variety of generator models have been introduced, yet they tend to specialize in a limited number of tasks. these include multilingual news writing @xcite , question answering @xcite , and article and review writing @xcite . in comparison, our bust dataset incorporates diverse tasks, enabling a more comprehensive analysis of detection systems' capabilities. a comprehensive overview of the available datasets in this domain is given in appendix tab. 5. in contrast to other studies, our research focuses exclusively on instruction-tuned models, which the public is increasingly using through prompting interfaces. ## dataset this section describes in detail the datasets used in our research. for a comprehensive overview of these datasets, see tab. 1. our collected comparison corpus includes paired entries of partially contextualized instructions, as well as both humanwritten and mgt responses. multiple mgt responses are available for each instruction, generated by different models, as shown in fig. 1 . the specific models from which these responses are derived vary in complexity and range in size from 7b to over 100b parameters. details of the text generation models can be found in appendix tab. 10 . for a comprehensive comparison and robust analyses, we included models in different configurations: standard models, models we further trained ourselves, and models with or without parameterefficient fine-tuning (peft). ## ablation studies analyzing the detector performance across the ablation scenarios (see fig. 4 ), we found gpt-zero as the top performer consistently, with scores of 0.683, 0.692, and 0.652 in the uncontrolled, temperaturemin, and length scenarios, respectively. following closely is chatgpt_qa, which suffered a noticeable 17% drop in performance, particularly when length control is applied. xlmr_chatgpt, in the third position, exhibits a similar pattern. interestingly, the introduction of temperature control does not significantly affect detector performance. however, length control appears to be a critical factor, leading to performance reduction in all detectors except radar_vicuna7b, which demonstrates a slight improvement under this constraint. 9 extending the ablation analysis to the text and the corresponding generators, we made the following observations. in the length-based scenario, focusing on text characteristics revealed a notable increase in the overall percentage of nouns, coupled with a decrease in the percentage of verbs (see appendix fig. 25 ). additionally, the emphasis on time orientation was reduced (see appendix fig. 27 ). we did not observe significant changes in writer's attitudes. however, when evaluating the performance of detectors on texts generated by different models, a noticeable change was identified for chatgpt_qa. it struggled more to correctly detect text generated by gpt-4 (see appendix fig. 24 ). in the temperature-min scenario, an analysis of text characteristics showed a drastic increase in the average sentence length for llama-2-7b* (see appendix fig. 26 ). in relation to writer's attitudes, llama-2-7b* was significantly affected in a positive manner. it played a crucial role in tempering overexpressed emotions, such as curiosity, and contributed to enhanced convincingness (see appendix fig. 13 ). despite this, the text generated by llama-2-7b* was not harder to detect in this scenario (see appendix fig. 24 ). ## discussion analyzing detectors' performance across tasks (see tab. furthermore, when evaluating models with pronounced emotions (falcon-7b-dolly, guanaco-7b, and gpt-3.5), we identified a significant advantage of chatgpt_qa over llmdet, shown in the detector vs. generator f1-macro comparison. this observation aligns with the correlations observed between chatgpt_qa predictions and writers' attitudes, contrasting with llmdet, which exhibits minimal correlation with writers' attitudes (see appendix fig. 22 ). although radar_vicuna7b is the overall least performing model, it demonstrates competitive performance in stance and re-view_generation tasks. these tasks were generated by repurposing datasets less likely covered during llms training, which could suggest that radar_vicuna7b is less overfitted. in general, llmdet demonstrates more consistent performance across the tasks, while chatgpt_qa ranks as the second-best performer in certain tasks but falls short in others. specifically, chatgpt_qa performs well in qa-related tasks as anticipated, yet it underperforms in tasks such as informa-tion_extraction. when comparing detector versus surrogate model performances at the task level, we can notice that for brainstorming, general_qa, open_qa, creative_writing, surrogate model performance mirrors detector behavior (meaning that the good performance of a detector, typically chatgpt_qa, could also be well mimicked by the respective surrogate model) while this is not the case for the other tasks (stance_generation, information_extraction, closed_qa, classification, review_generation, summarization). particularly when llmdet best performs in a task (e.g., stance_generation, see tab. 2 and appendix fig. 30 ), its behavior is hard to explain with the surrogate model. this means that the considered features are very useful for simulating chatgpt_qa behavior and less so for llmdet, probably leading the llmdet surrogate to rely on unexpected features like punctuation notably, variations in the best-performing model across evaluations arise from differences in how the data was grouped. figure 2 and ## conclusion in this study, we present a benchmark for evaluating llm-generated text detectors. our dataset comprises 3180 instructions with corresponding human responses across 10 tasks from 3 diverse sources. synthetic responses were generated by prompting 7 different llms, resulting in a dataset exceeding these distinctive characteristics position our benchmark as a valuable resource for end-users seeking to select a detector that best suits their target text style and use case. notably, we have integrated all analyses into an automated pipeline, which enhances accessibility and usability for researchers and practitioners in the field.