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1. "Good Robot! Now Watch This!": Repurposing Reinforcement Learning for Task-to-Task Transfer

   Hundt, Andrew; Murali, Aditya; Hubli, Priyanka; Liu, Ran; Gopalan, Nakul; Gombolay, Matthew; Hager, Gregory D. (October 2021, Proceedings of Machine Learning Research)

   Aleksandra Faust, David Hsu (Ed.)

   Modern Reinforcement Learning (RL) algorithms are not sample efficient to train on multi-step tasks in complex domains, impeding their wider deployment in the real world. We address this problem by leveraging the insight that RL models trained to complete one set of tasks can be repurposed to complete related tasks when given just a handful of demonstrations. Based upon this insight, we propose See-SPOT-Run (SSR), a new computational approach to robot learning that enables a robot to complete a variety of real robot tasks in novel problem domains without task-specific training. SSR uses pretrained RL models to create vectors that represent model, task, and action relevance in demonstration and test scenes. SSR then compares these vectors via our Cycle Consistency Distance (CCD) metric to determine the next action to take. SSR completes 58% more task steps and 20% more trials than a baseline few-shot learning method that requires task-specific training. SSR also achieves a four order of magnitude improvement in compute efficiency and a 20% to three order of magnitude improvement in sample efficiency compared to the baseline and to training RL models from scratch. To our knowledge, we are the first to address multi-step tasks from demonstration on a real robot without task-specific training, where both the visual input and action space output are high dimensional. Code is available in the supplement. 

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2. Unified Semantic Typing with Meaningful Label Inference

   https://doi.org/10.18653/v1/2022.naacl-main.190  

   Huang, James Y.; Li, Bangzheng; Xu, Jiashu; Chen, Muhao (January 2022, Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies)

   Semantic typing aims at classifying tokens or spans of interest in a textual context into semantic categories such as relations, entity types, and event types. The inferred labels of semantic categories meaningfully interpret how machines understand components of text. In this paper, we present UniST, a unified framework for semantic typing that captures label semantics by projecting both inputs and labels into a joint semantic embedding space. To formulate different lexical and relational semantic typing tasks as a unified task, we incorporate task descriptions to be jointly encoded with the input, allowing UniST to be adapted to different tasks without introducing task-specific model components. UniST optimizes a margin ranking loss such that the semantic relatedness of the input and labels is reflected from their embedding similarity. Our experiments demonstrate that UniST achieves strong performance across three semantic typing tasks: entity typing, relation classification and event typing. Meanwhile, UniST effectively transfers semantic knowledge of labels and substantially improves generalizability on inferring rarely seen and unseen types. In addition, multiple semantic typing tasks can be jointly trained within the unified framework, leading to a single compact multi-tasking model that performs comparably to dedicated single-task models, while offering even better transferability. 

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3. In-Context Learning Unlocked for Diffusion Models

   Wang, Zhendong; Jiang, Yifan; Lu, Yadong; Shen, Yelong; He, Pengcheng; Chen, Weizhu; Wang, Zhangyang; Zhou, Mingyuan (December 2023, Neural Information Processing Systems)

   We present Prompt Diffusion, a framework for enabling in-context learning in diffusion-based generative models. Given a pair of task-specific example images, such as depth from/to image and scribble from/to image, and a text guidance, our model automatically understands the underlying task and performs the same task on a new query image following the text guidance. To achieve this, we propose a vision-language prompt that can model a wide range of vision-language tasks and a diffusion model that takes it as input. The diffusion model is trained jointly on six different tasks using these prompts. The resulting Prompt Diffusion model becomes the first diffusion-based vision-language foundation model capable of in-context learning. It demonstrates high-quality in-context generation for the trained tasks and effectively generalizes to new, unseen vision tasks using their respective prompts. Our model also shows compelling text-guided image editing results. Our framework aims to facilitate research into in-context learning for computer vision. We share our code and pre-trained models at https://github. com/Zhendong-Wang/Prompt-Diffusion. 

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4. On the Power of Pre-Trained Text Representations: Models and Applications in Text Mining

   https://doi.org/10.1145/3447548.3470810  

   Meng, Yu; Huang, Jiaxin; Zhang, Yu; Han, Jiawei (August 2021, KDD'21:The 27th {ACM} {SIGKDD} Conference on Knowledge Discovery and Data Mining, August 14-18, 2021)

   null (Ed.)

   Recent years have witnessed the enormous success of text representation learning in a wide range of text mining tasks. Earlier word embedding learning approaches represent words as fixed low-dimensional vectors to capture their semantics. The word embeddings so learned are used as the input features of task-specific models. Recently, pre-trained language models (PLMs), which learn universal language representations via pre-training Transformer-based neural models on large-scale text corpora, have revolutionized the natural language processing (NLP) field. Such pre-trained representations encode generic linguistic features that can be transferred to almost any text-related applications. PLMs outperform previous task-specific models in many applications as they only need to be fine-tuned on the target corpus instead of being trained from scratch. In this tutorial, we introduce recent advances in pre-trained text embeddings and language models, as well as their applications to a wide range of text mining tasks. Specifically, we first overview a set of recently developed self-supervised and weakly-supervised text embedding methods and pre-trained language models that serve as the fundamentals for downstream tasks. We then present several new methods based on pre-trained text embeddings and language models for various text mining applications such as topic discovery and text classification. We focus on methods that are weakly-supervised, domain-independent, language-agnostic, effective and scalable for mining and discovering structured knowledge from large-scale text corpora. Finally, we demonstrate with real world datasets how pre-trained text representations help mitigate the human annotation burden and facilitate automatic, accurate and efficient text analyses. 

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5. Prompting as Panacea? A Case Study of In-Context Learning Performance for Qualitative Coding of Classroom Dialog

   https://doi.org/10.5281/zenodo.12729966  

   Ganesh, Ananya; Ch, Chelsea; ler; D'Mello, Sidney; Palmer, Martha; Kann, Katharina (January 2024, International Educational Data Mining Society)

   Benjamin, Paaßen; Carrie, Demmans Epp (Ed.)

   One of the areas where Large Language Models (LLMs) show promise is for automated qualitative coding, typically framed as a text classification task in natural language processing (NLP). Their demonstrated ability to leverage in-context learning to operate well even in data-scarce settings poses the question of whether collecting and annotating large-scale data for training qualitative coding models is still beneficial. In this paper, we empirically investigate the performance of LLMs designed for use in prompting-based in-context learning settings, and draw a comparison to models that have been trained using the traditional pretraining--finetuning paradigm with task-specific annotated data, specifically for tasks involving qualitative coding of classroom dialog. Compared to other domains where NLP studies are typically situated, classroom dialog is much more natural and therefore messier. Moreover, tasks in this domain are nuanced and theoretically grounded and require a deep understanding of the conversational context. We provide a comprehensive evaluation across five datasets, including tasks such as talkmove prediction and collaborative problem solving skill identification. Our findings show that task-specific finetuning strongly outperforms in-context learning, showing the continuing need for high-quality annotated training datasets. 

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