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- All Subjects: Computer Science
- Creators: Baral, Chitta
Description
Machine learning models can pick up biases and spurious correlations from training data and projects and amplify these biases during inference, thus posing significant challenges in real-world settings. One approach to mitigating this is a class of methods that can identify filter out bias-inducing samples from the training datasets to force models to avoid being exposed to biases. However, the filtering leads to a considerable wastage of resources as most of the dataset created is discarded as biased. This work deals with avoiding the wastage of resources by identifying and quantifying the biases. I further elaborate on the implications of dataset filtering on robustness (to adversarial attacks) and generalization (to out-of-distribution samples). The findings suggest that while dataset filtering does help to improve OOD(Out-Of-Distribution) generalization, it has a significant negative impact on robustness to adversarial attacks. It also shows that transforming bias-inducing samples into adversarial samples (instead of eliminating them from the dataset) can significantly boost robustness without sacrificing generalization.
ContributorsSachdeva, Bhavdeep Singh (Author) / Baral, Chitta (Thesis advisor) / Liu, Huan (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2021
Description
Instructional prompts are a novel technique that can significantly improve the performance of natural language processing tasks by specifying the task instruction to the language model. This is the first paper that uses instructional prompts to improve performance of the question answering task in biomedical domain. This work makes two significant contributions. Firstly, a question answer dataset of 600K question answer pairs has been developed by using the medical textbook ‘Differential Diagnosis Primary Care’, which contains information on how to diagnose a patient by observing their disease symptoms. Secondly, a question answering language model augmented with instructional prompts has been developed by training on the medical information extracted from the book ‘Differential Diagnosis Primary Care’. Experiments have been conducted to demonstrate that it performs better than a normal question answering model that does not use instructional prompts. Instructional prompts are based on prompt tuning and prefix tuning, which are novel techniques which can help train language model to do specific downstream tasks by keeping majority of model parameters frozen, and only optimizing a small number of continuous task-specific vectors (called the prefixes).
ContributorsSaxena, Sharad (Author) / Baral, Chitta (Thesis advisor) / Blanco, Eduardo (Committee member) / Anwar, Saadat (Committee member) / Arizona State University (Publisher)
Created2021
Description
Multimodal reasoning is one of the most interesting research fields because of the ability to interact with systems and the explainability of the models' behavior. Traditional multimodal research problems do not focus on complex commonsense reasoning (such as physical interactions). Although real-world objects have physical properties associated with them, many of these properties (such as mass and coefficient of friction) are not captured directly by the imaging pipeline. Videos often capture objects, their motion, and the interactions between different objects. However, these properties can be estimated by utilizing cues from relative object motion and the dynamics introduced by collisions. This thesis introduces a new video question-answering task for reasoning about the implicit physical properties of objects in a scene, from videos. For this task, I introduce a dataset -- CRIPP-VQA (Counterfactual Reasoning about Implicit Physical Properties - Video Question Answering), which contains videos of objects in motion, annotated with hypothetical/counterfactual questions about the effect of actions (such as removing, adding, or replacing objects), questions about planning (choosing actions to perform to reach a particular goal), as well as descriptive questions about the visible properties of objects. Further, I benchmark the performance of existing video question-answering models on two test settings of CRIPP-VQA: i.i.d. and an out-of-distribution setting which contains objects with values of mass, coefficient of friction, and initial velocities that are not seen in the training distribution. Experiments reveal a surprising and significant performance gap in terms of answering questions about implicit properties (the focus of this thesis) and explicit properties (the focus of prior work) of objects.
ContributorsPatel, Maitreya Jitendra (Author) / Yang, Yezhou (Thesis advisor) / Baral, Chitta (Committee member) / Lee, Kookjin (Committee member) / Arizona State University (Publisher)
Created2022
Description
Interpreting answers to yes-no questions in social media is difficult. Yes and no keywords are uncommon, and when answers include them, they are rarely to be interpreted what the keywords suggest. This work presents a new corpus of 4,442 yes-no question answer pairs from Twitter (Twitter-YN). The corpus includes question-answer instances from different temporal settings. These settings allow investigating if having older tweets helps understanding more contemporary tweets. Common linguistic features of answers meaning yes, no as well as those whose interpretation remains unknown are also discussed. Experimental results show that large language models are far from solving this problem, even after fine-tuning and blending other corpora for the same problem but outside social media (F1: 0.59). In addition to English, this work presents a Hindi corpus of 3,409 yes-no questions and answers from Twitter (Twitter-YN-hi). Cross lingual experiments are conducted using a distant supervision approach. It is observed that performance of multilingual large language models to interpret indirect answers to yes-no questions in Hindi can be improved when Twitter-YN is blended with distantly supervised data.
ContributorsMathur, Shivam (Author) / Blanco, Eduardo (Thesis advisor) / Baral, Chitta (Thesis advisor) / Choi, YooJung (Committee member) / Arizona State University (Publisher)
Created2023
Description
In natural language processing, language models have achieved remarkable success over the last few years. The Transformers are at the core of most of these models. Their success can be mainly attributed to an enormous amount of curated data they are trained on. Even though such language models are trained on massive curated data, they often need specific extracted knowledge to understand better and reason. This is because often relevant knowledge may be implicit or missing, which hampers machine reasoning. Apart from that, manual knowledge curation is time-consuming and erroneous. Hence, finding fast and effective methods to extract such knowledge from data is important for improving language models. This leads to finding ideal ways to utilize such knowledge by incorporating them into language models. Successful knowledge extraction and integration lead to an important question of knowledge evaluation of such models by developing tools or introducing challenging test suites to learn about their limitations and improve them further. So to improve the transformer-based models, understanding the role of knowledge becomes important. In the pursuit to improve language models with knowledge, in this dissertation I study three broad research directions spanning across the natural language, biomedical and cybersecurity domains: (1) Knowledge Extraction (KX) - How can transformer-based language models be leveraged to extract knowledge from data? (2) Knowledge Integration (KI) - How can such specific knowledge be used to improve such models? (3) Knowledge Evaluation (KE) - How can language models be evaluated for specific skills and understand their limitations?
I propose methods to extract explicit textual, implicit structural, missing textual, and missing structural knowledge from natural language and binary programs using transformer-based language models. I develop ways to improve the language model’s multi-step and commonsense reasoning abilities using external knowledge. Finally, I develop challenging datasets which assess their numerical reasoning skills in both in-domain and out-of-domain settings.
ContributorsPal, Kuntal Kumar (Author) / Baral, Chitta (Thesis advisor) / Wang, Ruoyu (Committee member) / Blanco, Eduardo (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2023
Description
One of the challenges in Artificial Intelligence (AI) is to integrate fast, automatic, and intuitive System-1 thinking with slow, deliberate, and logical System-2 thinking. While deep learning approaches excel at perception tasks for System-1, their reasoning capabilities for System-2 are limited. Besides, deep learning approaches are usually data-hungry, hard to make use of explicit knowledge, and struggling with interpretability and justification. This dissertation presents three neuro-symbolic AI approaches that integrate neural networks (NNs) with symbolic AI methods to address these issues. The first approach presented in this dissertation is NeurASP, which combines NNs with Answer Set Programming (ASP), a logic programming formalism. NeurASP provides an effective way to integrate sub-symbolic and symbolic computation by treating NN outputs as probability distributions over atomic facts in ASP. The explicit knowledge encoded in ASP corrects mistakes in NN outputs and allows for better training with less data. To avoid NeurASP's bottleneck in symbolic computation, this dissertation presents a Constraint Loss via Straight-Through Estimators (CL-STE). CL-STE provides a systematic way to compile discrete logical constraints into a loss function over discretized NN outputs and scales significantly better than state-of-the-art neuro-symbolic methods. This dissertation also presents a finding when CL-STE was applied to Transformers. Transformers can be extended with recurrence to enhance its power for multi-step reasoning. Such Recurrent Transformer can straightforwardly be applied to visual constraint reasoning problems while successfully addressing the symbol grounding problem. Lastly, this dissertation addresses the limitation of pre-trained Large Language Models (LLMs) on multi-step logical reasoning problems with a dual-process neuro-symbolic reasoning system called LLM+ASP, where an LLM (e.g., GPT-3) serves as a highly effective few-shot semantic parser that turns natural language sentences into a logical form that can be used as input to ASP. LLM+ASP achieves state-of-the-art performance on several textual reasoning benchmarks and can handle robot planning tasks that an LLM alone fails to solve.
ContributorsYang, Zhun (Author) / Lee, Joohyung (Thesis advisor) / Baral, Chitta (Committee member) / Li, Baoxin (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2023
Description
An important objective of AI is to understand real-world observations and build up interactive communication with people. The ability to interpret and react to the perception reveals the important necessity of developing such a system across both the modalities of Vision (V) and Language (L). Although there have been massive efforts on various VL tasks, e.g., Image/Video Captioning, Visual Question Answering, and Textual Grounding, very few of them focus on building the VL models with increased efficiency under real-world scenarios. The main focus of this dissertation is to comprehensively investigate the very uncharted efficient VL learning, aiming to build lightweight, data-efficient, and real-world applicable VL models. The proposed studies in this dissertation take three primary aspects into account when it comes to efficient VL, 1). Data Efficiency: collecting task-specific annotations is prohibitively expensive and so manual labor is not always attainable. Techniques are developed to assist the VL learning from implicit supervision, i.e., in a weakly- supervised fashion. 2). Continuing from that, efficient representation learning is further explored with increased scalability, leveraging a large image-text corpus without task-specific annotations. In particular, the knowledge distillation technique is studied for generic Representation Learning which proves to bring substantial performance gain to the regular representation learning schema. 3). Architectural Efficiency. Deploying the VL model on edge devices is notoriously challenging due to their cumbersome architectures. To further extend these advancements to the real world, a novel efficient VL architecture is designed to tackle the inference bottleneck and the inconvenient two-stage training. Extensive discussions have been conducted on several critical aspects that prominently influence the performances of compact VL models.
ContributorsFang, Zhiyuan (Author) / Yang, Yezhou (Thesis advisor) / Baral, Chitta (Committee member) / Liu, Huan (Committee member) / Liu, Zicheng (Committee member) / Arizona State University (Publisher)
Created2022
Description
T-cells are an integral component of the immune system, enabling the body to distinguish between pathogens and the self. The primary mechanism which enables this is their T-cell receptors (TCR) which bind to antigen epitopes foreign to the body. This detection mechanism allows the T-cell to determine when an immune response is necessary. The computational prediction of TCR-epitope binding is important to researchers for both medical applications and for furthering their understanding of the biological mechanisms that impact immunity. Models which have been developed for this purpose fail to account for the interrelationships between amino acids and demonstrate poor out-of-sample performance. Small changes to the amino acids in these protein sequences can drastically change their structure and function. In recent years, attention-based deep learning models have shown success in their ability to learn rich contextual representations of data. To capture the contextual biological relationships between the amino acids, a multi-head self-attention model was created to predict the binding affinity between given TCR and epitope sequences. By learning the structural nuances of the sequences, this model is able to improve upon existing model performance and grant insights into the underlying mechanisms which impact binding.
ContributorsCai, Michael Ray (Author) / Lee, Heewook (Thesis advisor) / Bang, Seojin (Committee member) / Baral, Chitta (Committee member) / Arizona State University (Publisher)
Created2021
Description
Artificial Intelligence, as the hottest research topic nowadays, is mostly driven by data. There is no doubt that data is the king in the age of AI. However, natural high-quality data is precious and rare. In order to obtain enough and eligible data to support AI tasks, data processing is always required. To be even worse, the data preprocessing tasks are often dull and heavy, which require huge human labors to deal with. Statistics show 70% - 80% of the data scientists' time is spent on data integration process. Among various reasons, schema changes that commonly exist in the data warehouse are one significant obstacle that impedes the automation of the end-to-end data integration process. Traditional data integration applications rely on data processing operators such as join, union, aggregation and so on. Those operations are fragile and can be easily interrupted by schema changes. Whenever schema changes happen, the data integration applications will require human labors to solve the interruptions and downtime. The industries as well as the data scientists need a new mechanism to handle the schema changes in data integration tasks. This work proposes a new direction of data integration applications based on deep learning models. The data integration problem is defined in the scenario of integrating tabular-format data with natural schema changes, using the cell-based data abstraction. In addition, data augmentation and adversarial learning are investigated to boost the model robustness to schema changes. The experiments are tested on two real-world data integration scenarios, and the results demonstrate the effectiveness of the proposed approach.
ContributorsWang, Zijie (Author) / Zou, Jia (Thesis advisor) / Baral, Chitta (Committee member) / Candan, K. Selcuk (Committee member) / Arizona State University (Publisher)
Created2021
Description
Enabling robots to physically engage with their environment in a safe and efficient manner is an essential step towards human-robot interaction. To date, robots usually operate as pre-programmed workers that blindly execute tasks in highly structured environments crafted by skilled engineers. Changing the robots’ behavior to cover new duties or handle variability is an expensive, complex, and time-consuming process. However, with the advent of more complex sensors and algorithms, overcoming these limitations becomes within reach. This work proposes innovations in artificial intelligence, language understanding, and multimodal integration to enable next-generation grasping and manipulation capabilities in autonomous robots. The underlying thesis is that multimodal observations and instructions can drastically expand the responsiveness and dexterity of robot manipulators. Natural language, in particular, can be used to enable intuitive, bidirectional communication between a human user and the machine. To this end, this work presents a system that learns context-aware robot control policies from multimodal human demonstrations. Among the main contributions presented are techniques for (a) collecting demonstrations in an efficient and intuitive fashion, (b) methods for leveraging physical contact with the environment and objects, (c) the incorporation of natural language to understand context, and (d) the generation of robust robot control policies. The presented approach and systems are evaluated in multiple grasping and manipulation settings ranging from dexterous manipulation to pick-and-place, as well as contact-rich bimanual insertion tasks. Moreover, the usability of these innovations, especially when utilizing human task demonstrations and communication interfaces, is evaluated in several human-subject studies.
ContributorsStepputtis, Simon (Author) / Ben Amor, Heni (Thesis advisor) / Baral, Chitta (Committee member) / Yang, Yezhou (Committee member) / Lee, Stefan (Committee member) / Arizona State University (Publisher)
Created2021