Review-LLM: Harnessing Large Language Models for Personalized Review Generation

Product review generation is an important task in recommender systems, which could provide explanation and persuasiveness for the recommendation. Recently, Large Language Models (LLMs, e.g., ChatGPT) have shown superior text modeling and generating ability, which could be applied in review generation. However, directly applying the LLMs for generating reviews might be troubled by the ``polite'' phenomenon of the LLMs and could not generate personalized reviews (e.g., negative reviews). In this paper, we propose Review-LLM that customizes LLMs for personalized review generation. Firstly, we construct the prompt input by aggregating user historical behaviors, which include corresponding item titles and reviews. This enables the LLMs to capture user interest features and review writing style. Secondly, we incorporate ratings as indicators of satisfaction into the prompt, which could further improve the model's understanding of user preferences and the sentiment tendency control of generated reviews. Finally, we feed the prompt text into LLMs, and use Supervised Fine-Tuning (SFT) to make the model generate personalized reviews for the given user and target item. Experimental results on the real-world dataset show that our fine-tuned model could achieve better review generation performance than existing close-source LLMs.

Bucket Pre-training is All You Need

Large language models (LLMs) have demonstrated exceptional performance across various natural language processing tasks. However, the conventional fixed-length data composition strategy for pretraining, which involves concatenating and splitting documents, can introduce noise and limit the model's ability to capture long-range dependencies. To address this, we first introduce three metrics for evaluating data composition quality: padding ratio, truncation ratio, and concatenation ratio. We further propose a multi-bucket data composition method that moves beyond the fixed-length paradigm, offering a more flexible and efficient approach to pretraining. Extensive experiments demonstrate that our proposed method could significantly improving both the efficiency and efficacy of LLMs pretraining. Our approach not only reduces noise and preserves context but also accelerates training, making it a promising solution for LLMs pretraining.

Meaningful Learning: Advancing Abstract Reasoning in Large Language Models via Generic Fact Guidance

Large language models (LLMs) have developed impressive performance and strong explainability across various reasoning scenarios, marking a significant stride towards mimicking human-like intelligence. Despite this, when tasked with simple questions supported by a generic fact, LLMs often fail to provide consistent and precise answers, indicating a deficiency in abstract reasoning abilities. This has sparked a vigorous debate about whether LLMs are genuinely reasoning or merely memorizing. In light of this, we design a preliminary study to quantify and delve into the abstract reasoning abilities of existing LLMs. Our findings reveal a substantial discrepancy between their general reasoning and abstract reasoning performances. To relieve this problem, we tailor an abstract reasoning dataset (AbsR) together with a meaningful learning paradigm to teach LLMs how to leverage generic facts for reasoning purposes. The results show that our approach not only boosts the general reasoning performance of LLMs but also makes considerable strides towards their capacity for abstract reasoning, moving beyond simple memorization or imitation to a more nuanced understanding and application of generic facts.

PEPT: Expert Finding Meets Personalized Pre-training

Finding appropriate experts is essential in Community Question Answering (CQA) platforms as it enables the effective routing of questions to potential users who can provide relevant answers. The key is to personalized learning expert representations based on their historical answered questions, and accurately matching them with target questions. There have been some preliminary works exploring the usability of PLMs in expert finding, such as pre-training expert or question representations. However, these models usually learn pure text representations of experts from histories, disregarding personalized and fine-grained expert modeling. For alleviating this, we present a personalized pre-training and fine-tuning paradigm, which could effectively learn expert interest and expertise simultaneously. Specifically, in our pre-training framework, we integrate historical answered questions of one expert with one target question, and regard it as a candidate aware expert-level input unit. Then, we fuse expert IDs into the pre-training for guiding the model to model personalized expert representations, which can help capture the unique characteristics and expertise of each individual expert. Additionally, in our pre-training task, we design: 1) a question-level masked language model task to learn the relatedness between histories, enabling the modeling of question-level expert interest; 2) a vote-oriented task to capture question-level expert expertise by predicting the vote score the expert would receive. Through our pre-training framework and tasks, our approach could holistically learn expert representations including interests and expertise. Our method has been extensively evaluated on six real-world CQA datasets, and the experimental results consistently demonstrate the superiority of our approach over competitive baseline methods.

PUNR: Pre-training with User Behavior Modeling for News Recommendation

News recommendation aims to predict click behaviors based on user behaviors. How to effectively model the user representations is the key to recommending preferred news. Existing works are mostly focused on improvements in the supervised fine-tuning stage. However, there is still a lack of PLM-based unsupervised pre-training methods optimized for user representations. In this work, we propose an unsupervised pre-training paradigm with two tasks, i.e. user behavior masking and user behavior generation, both towards effective user behavior modeling. Firstly, we introduce the user behavior masking pre-training task to recover the masked user behaviors based on their contextual behaviors. In this way, the model could capture a much stronger and more comprehensive user news reading pattern. Besides, we incorporate a novel auxiliary user behavior generation pre-training task to enhance the user representation vector derived from the user encoder. We use the above pre-trained user modeling encoder to obtain news and user representations in downstream fine-tuning. Evaluations on the real-world news benchmark show significant performance improvements over existing baselines.

Deep Generative Modeling on Limited Data with Regularization by Nontransferable Pre-trained Models

Deep generative models (DGMs) are data-eager. Essentially, it is because learning a complex model on limited data suffers from a large variance and easily overfits. Inspired by the \emph{bias-variance dilemma}, we propose \emph{regularized deep generative model} (Reg-DGM), which leverages a nontransferable pre-trained model to reduce the variance of generative modeling with limited data. Formally, Reg-DGM optimizes a weighted sum of a certain divergence between the data distribution and the DGM and the expectation of an energy function defined by the pre-trained model w.r.t. the DGM. Theoretically, we characterize the existence and uniqueness of the global minimum of Reg-DGM in the nonparametric setting and rigorously prove the statistical benefits of Reg-DGM w.r.t. the mean squared error and the expected risk in a simple yet representative Gaussian-fitting example. Empirically, it is quite flexible to specify the DGM and the pre-trained model in Reg-DGM. In particular, with a ResNet-18 classifier pre-trained on ImageNet and a data-dependent energy function, Reg-DGM consistently improves the generation performance of strong DGMs including StyleGAN2 and ADA on several benchmarks with limited data and achieves competitive results to the state-of-the-art methods.

Going Negative Online? -- A Study of Negative Advertising on Social Media

A growing number of empirical studies suggest that negative advertising is effective in campaigning, while the mechanisms are rarely mentioned. With the scandal of Cambridge Analytica and Russian intervention behind the Brexit and the 2016 presidential election, people have become aware of the political ads on social media and have pressured congress to restrict political advertising on social media. Following the related legislation, social media companies began disclosing their political ads archive for transparency during the summer of 2018 when the midterm election campaign was just beginning. This research collects the data of the related political ads in the context of the U.S. midterm elections since August to study the overall pattern of political ads on social media and uses sets of machine learning methods to conduct sentiment analysis on these ads to classify the negative ads. A novel approach is applied that uses AI image recognition to study the image data. Through data visualization, this research shows that negative advertising is still the minority, Republican advertisers and third party organizations are more likely to engage in negative advertising than their counterparts. Based on ordinal regressions, this study finds that anger evoked information-seeking is one of the main mechanisms causing negative ads to be more engaging and effective rather than the negative bias theory. Overall, this study provides a unique understanding of political advertising on social media by applying innovative data science methods. Further studies can extend the findings, methods, and datasets in this study, and several suggestions are given for future research.

An Empirical Study towards Characterizing Deep Learning Development and Deployment across Different Frameworks and Platforms

Deep Learning (DL) has recently achieved tremendous success. A variety of DL frameworks and platforms play a key role to catalyze such progress. However, the differences in architecture designs and implementations of existing frameworks and platforms bring new challenges for DL software development and deployment. Till now, there is no study on how various mainstream frameworks and platforms influence both DL software development and deployment in practice. To fill this gap, we take the first step towards understanding how the most widely-used DL frameworks and platforms support the DL software development and deployment. We conduct a systematic study on these frameworks and platforms by using two types of DNN architectures and three popular datasets. (1) For development process, we investigate the prediction accuracy under the same runtime training configuration or same model weights/biases. We also study the adversarial robustness of trained models by leveraging the existing adversarial attack techniques. The experimental results show that the computing differences across frameworks could result in an obvious prediction accuracy decline, which should draw the attention of DL developers. (2) For deployment process, we investigate the prediction accuracy and performance (refers to time cost and memory consumption) when the trained models are migrated/quantized from PC to real mobile devices and web browsers. The DL platform study unveils that the migration and quantization still suffer from compatibility and reliability issues. Meanwhile, we find several DL software bugs by using the results as a benchmark. We further validate the results through bug confirmation from stakeholders and industrial positive feedback to highlight the implications of our study. Through our study, we summarize practical guidelines, identify challenges and pinpoint new research directions.

Neural Review Rating Prediction with Hierarchical Attentions and Latent Factors

Text reviews can provide rich useful semantic information for modeling users and items, which can benefit rating prediction in recommendation. Different words and reviews may have different informativeness for users or items. Besides, different users and items should be personalized. Most existing works regard all reviews equally or utilize a general attention mechanism. In this paper, we propose a hierarchical attention model fusing latent factor model for rating prediction with reviews, which can focus on important words and informative reviews. Specially, we use the factor vectors of Latent Factor Model to guide the attention network and combine the factor vectors with feature representation learned from reviews to predict the final ratings. Experiments on real-world datasets validate the effectiveness of our approach.

NRPA: Neural Recommendation with Personalized Attention

Existing review-based recommendation methods usually use the same model to learn the representations of all users/items from reviews posted by users towards items. However, different users have different preference and different items have different characteristics. Thus, the same word or similar reviews may have different informativeness for different users and items. In this paper we propose a neural recommendation approach with personalized attention to learn personalized representations of users and items from reviews. We use a review encoder to learn representations of reviews from words, and a user/item encoder to learn representations of users or items from reviews. We propose a personalized attention model, and apply it to both review and user/item encoders to select different important words and reviews for different users/items. Experiments on five datasets validate our approach can effectively improve the performance of neural recommendation.