Fine-tuning is widely used to adapt language models for specific goals, often leveraging real-world data such as patient records, customer-service interactions, or web content in languages not covered in pre-training. These datasets are typically massive, noisy, and often confidential, making their direct inspection challenging. However, understanding them is essential for guiding model deployment and informing decisions about data cleaning or suppressing any harmful behaviors learned during fine-tuning. In this study, we introduce the task of novelty discovery through generation, which aims to identify novel domains of a fine-tuning dataset by generating examples that illustrate these properties. Our approach － Contrastive Generative Exploration (CGE) － assumes no direct access to the data but instead relies on a pre-trained model and the same model after fine-tuning. By contrasting the predictions of these two models, CGE can generate examples that highlight novel domains of the fine-tuning data. However, this simple approach may produce examples that are too similar to one another, failing to capture the full range of novel domains present in the dataset. We address this by introducing an iterative version of CGE, where the previously generated examples are used to update the pre-trained model, and this updated model is then contrasted with the fully fine-tuned model to generate the next example, promoting diversity in the generated outputs. Our experiments demonstrate the effectiveness of CGE in detecting novel domains, such as toxic language, as well as new natural and programming languages. Furthermore, we show that CGE remains effective even when models are fine-tuned using differential privacy techniques.

We present UniDetox, a universally applicable method designed to mitigate toxicity across various large language models (LLMs). Previous detoxification methods are typically model-specific, addressing only individual models or model families, and require careful hyperparameter tuning due to the trade-off between detoxification efficacy and language modeling performance. In contrast, UniDetox provides a detoxification technique that can be universally applied to a wide range of LLMs without the need for separate model-specific tuning. Specifically, we propose a novel and efficient dataset distillation technique for detoxification using contrastive decoding. This approach distills detoxifying representations in the form of synthetic text data, enabling universal detoxification of any LLM through fine-tuning with the distilled text. Our experiments demonstrate that the detoxifying text distilled from GPT-2 can effectively detoxify larger models, including OPT, Falcon, and LLaMA-2. Furthermore, UniDetox eliminates the need for separate hyperparameter tuning for each model, as a single hyperparameter configuration can be seamlessly applied across different models. Additionally, analysis of the detoxifying text reveals a reduction in politically biased content, providing insights into the attributes necessary for effective detoxification of LLMs.

Identifying the training datasets that influence a language model's outputs is essential for minimizing the generation of harmful content and enhancing its performance.Ideally, we can measure the influence of each dataset by removing it from training; however, it is prohibitively expensive to retrain a model multiple times.This paper presents UnTrac: Unlearning Traces the influence of a training dataset on the model's performance.UnTrac is extremely simple; each training dataset is unlearned by gradient ascent, and we evaluate how much the model's predictions change after unlearning.Furthermore, we propose a more scalable approach, UnTrac-Inv, which unlearns a test dataset and evaluates the unlearned model on training datasets.UnTrac-Inv resembles Un-Trac, while being efficient for massive training datasets.In the experiments, we examine if our methods can assess the influence of pretraining datasets on generating toxic, biased, and untruthful content.Our methods estimate their influence much more accurately than existing methods while requiring neither excessive memory space nor multiple checkpoints.

Abstract This paper presents a novel unsupervised abstractive summarization method for opinionated texts. While the basic variational autoencoder-based models assume a unimodal Gaussian prior for the latent code of sentences, we alternate it with a recursive Gaussian mixture, where each mixture component corresponds to the latent code of a topic sentence and is mixed by a tree-structured topic distribution. By decoding each Gaussian component, we generate sentences with tree-structured topic guidance, where the root sentence conveys generic content, and the leaf sentences describe specific topics. Experimental results demonstrate that the generated topic sentences are appropriate as a summary of opinionated texts, which are more informative and cover more input contents than those generated by the recent unsupervised summarization model (Bražinskas et al., 2020). Furthermore, we demonstrate that the variance of latent Gaussians represents the granularity of sentences, analogous to Gaussian word embedding (Vilnis and McCallum, 2015).

This paper presents a tree-structured neural topic model, which has a topic distribution over a tree with an infinite number of branches.Our model parameterizes an unbounded ancestral and fraternal topic distribution by applying doubly-recurrent neural networks.With the help of autoencoding variational Bayes, our model improves data scalability and achieves competitive performance when inducing latent topics and tree structures, as compared to a prior tree-structured topic model (Blei et al., 2010).This work extends the tree-structured topic model such that it can be incorporated with neural models for downstream tasks.

This paper focuses on the end-to-end abstractive summarization of a single product review without supervision.We assume that a review can be described as a discourse tree, in which the summary is the root, and the child sentences explain their parent in detail.By recursively estimating a parent from its children, our model learns the latent discourse tree without an external parser and generates a concise summary.We also introduce an architecture that ranks the importance of each sentence on the tree to support summary generation focusing on the main review point.The experimental results demonstrate that our model is competitive with or outperforms other unsupervised approaches.In particular, for relatively long reviews, it achieves a competitive or better performance than supervised models.The induced tree shows that the child sentences provide additional information about their parent, and the generated summary abstracts the entire review.

The need for automatic document summarization that can be used for practical applications is increasing rapidly.In this paper, we propose a general framework for summarization that extracts sentences from a document using externally related information.Our work is aimed at single document summarization using small amounts of reference summaries.In particular, we address document summarization in the framework of multitask learning using curriculum learning for sentence extraction and document classification.The proposed framework enables us to obtain better feature representations to extract sentences from documents.We evaluate our proposed summarization method on two datasets: financial report and news corpus.Experimental results demonstrate that our summarizers achieve performance that is comparable to stateof-the-art systems.