Spurthi Amba Hombaiah
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PRewrite: Prompt Rewriting with Reinforcement Learning
Qiaozhu Mei
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (2024) (to appear)
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Prompt engineering is critical for the development of LLM-based applications. However, it is usually done manually in a "trial and error" fashion that can be time consuming, ineffective, and sub-optimal. Even for the prompts which seemingly work well, there is always a lingering question: can the prompts be made better with further modifications?
To address these problems, we investigate automated prompt engineering in this paper. Specifically, we propose PRewrite, an automated method to rewrite an under-optimized prompt to a more effective prompt. We instantiate the prompt rewriter using an LLM. The rewriter LLM is trained using reinforcement learning to optimize the performance on a given downstream task. We conduct experiments on diverse benchmark datasets, which demonstrates the effectiveness of PRewrite.
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Creator Context for Tweet Recommendation
Matt Colen
Sergey Levi
Vladimir Ofitserov
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track
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When discussing a tweet, people usually not only refer to the content it delivers, but also to the person behind the tweet. In other words, grounding the interpretation of the tweet in the context of its creator plays an important role in deciphering the true intent and the importance of the tweet.
In this paper, we attempt to answer the question of how creator context should be used to advance tweet understanding. Specifically, we investigate the usefulness of different types of creator context, and examine different model structures for incorporating creator context in tweet modeling. We evaluate our tweet understanding models on a practical use case -- recommending relevant tweets to news articles. This use case already exists in popular news apps, and can also serve as a useful assistive tool for journalists. We discover that creator context is essential for tweet understanding, and can improve application metrics by a large margin. However, we also observe that not all creator contexts are equal. Creator context can be time sensitive and noisy. Careful creator context selection and deliberate model structure design play an important role in creator context effectiveness.
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Preview abstract
The content on the web is in a constant state of flux. New entities, issues, and ideas continuously emerge, while the semantics of the existing conversation topics gradually shift. In recent years, pretrained language models like BERT greatly improved the state-of-the-art for a large spectrum of content understanding tasks. Therefore, in this paper, we aim to study how these language models can be adapted to better handle continuously evolving web content. In our study, we first analyze the evolution of 2013 – 2019 Twitter data, and unequivocally confirm that a BERT model trained on past tweets would heavily deteriorate when directly applied to data from later years. Then, we investigate two possible sources of the deterioration: the semantic shift of existing tokens and the sub-optimal or failed understanding of new tokens. To this end, we both explore two different vocabulary composition methods, as well as propose three sampling methods which help in efficient incremental training for BERT-like models. Compared to a new model trained from scratch offline, our incremental training (a) reduces the training costs, (b) achieves better performance on evolving content, and (c) is suitable for online deployment. The superiority of our methods is validated using two downstream tasks. We demonstrate significant improvements when incrementally evolving the model from a particular base year, on the task of Country Hashtag Prediction, as well as on the OffensEval 2019 task.
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LAMPRET: Layout-Aware Multimodal PreTraining for Document Understanding
Te-Lin Wu
VIGIL-NAACL21 (2021)
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Document layout comprises both structural and visual (\eg font size) information that are vital but often ignored by machine learning models. The few existing models which do use layout information only consider \textit{textual} contents, and overlook the existence of contents in other modalities such as images. Additionally, spatial interactions of presented contents in a layout was never fully exploited. On the other hand, a series of document understanding tasks are calling out for layout information. One example is given a position in a document, which image is the best to fit in.
To address current models' limitations and tackle layout-aware document understanding tasks, we first parse a document into blocks whose content can be textual, tabular, or multimedia (\eg images) using a proprietary tool. We then propose a novel hierarchical framework, LAMPreT, to encode the blocks.
Our LAMPreT model encodes each block with a multimodal transformer in the lower-level, and aggregates the block-level representations and connections utilizing a specifically designed transformer at the higher-level.
We design hierarchical pre-training objectives where the lower-level model is trained with the standard masked language modeling (MLM) loss and the multimodal alignment loss, and the higher-level model is trained with three layout-aware objectives:
(1) block-order predictions,
(2) masked block predictions, and
(3) image fitting predictions.
We test the proposed model on two layout-aware tasks -- image suggestions and text block filling, and show the effectiveness of our proposed hierarchical architecture as well as pre-training techniques.
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