Xianjun Yang works at University of California, Santa Barbara.

Posts

DNA-GPT: Divergent N-Gram Analysis for Training-Free Detection of GPT-Generated Text

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Large language models (LLMs) have notably enhanced the fluency and diversity of machine-generated text. However, this progress also presents a significant challenge in detecting the origin of a given text, and current research on detection methods lags behind the rapid evolution of LLMs. Conventional training-based methods have limitations in flexibility, particularly when adapting to new domains, and they often lack explanatory power. To address this gap, we propose a novel training-free detection strategy called Divergent N-Gram Analysis (DNA-GPT). Given a text, we first truncate it in the middle and then use only the preceding portion as input to the LLMs to regenerate the new remaining parts. By analyzing the differences between the original and new remaining parts through N-gram analysis in black-box or probability divergence in white-box, we can clearly illustrate significant discrepancies between machine-generated and human-written text. We conducted extensive experiments on the most advanced LLMs from OpenAI, including text-davinci-003, GPT-3.5-turbo, and GPT-4, as well as open-source models such as GPT-NeoX-20B and LLaMa-13B. Results show that our zero-shot approach exhibits state-of-the-art performance in distinguishing between human and GPT-generated text on four English and one German dataset, outperforming OpenAI’s own classifier, which is trained on millions of text. Additionally, our methods provide reasonable explanations and evidence to support our claim, which is a unique feature of explainable detection. Our method is also robust under the revised text attack and can additionally solve model sourcing.

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Dynamic Prompting: A Unified Framework for Prompt Tuning

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It has been demonstrated that prompt tuning is highly effective in efficiently eliciting knowledge from language models (LMs). However, the prompt tuning still lags behind fine tuning, especially when the LMs are small. P tuning v2 (Liu et al., 2021b) makes it comparable with finetuning by adding continuous prompts for every layer of the pre trained model. However, prepending fixed soft prompts for all instances, regardless of their discrepancy, is doubtful. In particular, the inserted prompt position, length, and the representations ofprompts for diversified instances through different tasks could all affect the prompt tuning performance. To fill this gap, we propose dynamic prompting (DP): the position, length, and prompt representation can all be dynamically optimized with respect to different tasks and instances. We conduct comprehensive experiments on the SuperGlue benchmark tovalidate our hypothesis and demonstrate substantial improvements. We also derive a unified framework for supporting our dynamic prompting strategy. In particular, we use a simple learning network and Gumble Softmax for learning instance dependent guidance. Experimental results show that simple instance level position aware soft prompts can improve the classification accuracy of up to 6 points on average on five datasets, reducing its gap with fine tuning. Besides, we also prove its universal usefulness under full data, few shot, andmultitask regimes. Combining them together can even further unleash the power of DP, narrowing the distance between fine tuning.

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Exploring the limits of ChatGPT for Query or Aspect based Text Summarization

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Text summarization has been a crucial problem in natural language processing (NLP) for several decades. It aims to condense lengthy documents into shorter versions while retaining the most critical information. Various methods have been proposed for text summarization, including extractive and abstractive summarization. The emergence of large language models (LLMs) like GPT3 and ChatGPT has recently created significant interest in using these models for text summarization tasks. Recent studies (Goyal et al., 2022, Zhang et al., 2023) have shown that LLMs generated news summaries are already on par with humans. However, the performance of LLMs for more practical applications like aspect or query based summaries is underexplored. To fill this gap, we conducted an evaluation of ChatGPT’s performance on four widely used benchmark datasets, encompassing diverse summaries from Reddit posts, news articles, dialogue meetings, and stories. Our experiments reveal that ChatGPT’s performance is comparable to traditional fine tuning methods in terms of Rouge scores. Moreover, we highlight some unique differences between ChatGPT generated summaries and human references, providing valuable insights into the superpower of ChatGPT for diverse text summarization tasks. Our findings call for new directions in this area, and we plan to conduct further research to systematically examine the characteristics of ChatGPT generated summaries through extensive human evaluation.

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