Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning

Most successful information extraction systems operate with access to a large collection of documents. In this work, we explore the task of acquiring and incorporating external evidence to improve extraction accuracy in domains where the amount of training data is scarce. This process entails issuing search queries, extraction from new sources and reconciliation of extracted values, which are repeated until sufficient evidence is collected. We approach the problem using a reinforcement learning framework where our model learns to select optimal actions based on contextual information. We employ a deep Q-network, trained to optimize a reward function that reflects extraction accuracy while penalizing extra effort. Our experiments on two databases -- of shooting incidents, and food adulteration cases -- demonstrate that our system significantly outperforms traditional extractors and a competitive meta-classifier baseline.Comment: Appearing in EMNLP 2016 (12 pages incl. supplementary material

LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models

Recent advancements in text-to-image generation with diffusion models have yielded remarkable results synthesizing highly realistic and diverse images. However, these models still encounter difficulties when generating images from prompts that demand spatial or common sense reasoning. We propose to equip diffusion models with enhanced reasoning capabilities by using off-the-shelf pretrained large language models (LLMs) in a novel two-stage generation process. First, we adapt an LLM to be a text-guided layout generator through in-context learning. When provided with an image prompt, an LLM outputs a scene layout in the form of bounding boxes along with corresponding individual descriptions. Second, we steer a diffusion model with a novel controller to generate images conditioned on the layout. Both stages utilize frozen pretrained models without any LLM or diffusion model parameter optimization. We validate the superiority of our design by demonstrating its ability to outperform the base diffusion model in accurately generating images according to prompts that necessitate both language and spatial reasoning. Additionally, our method naturally allows dialog-based scene specification and is able to handle prompts in a language that is not well-supported by the underlying diffusion model.Comment: Work in progres

Conformal Language Modeling

We propose a novel approach to conformal prediction for generative language models (LMs). Standard conformal prediction produces prediction sets -- in place of single predictions -- that have rigorous, statistical performance guarantees. LM responses are typically sampled from the model's predicted distribution over the large, combinatorial output space of natural language. Translating this process to conformal prediction, we calibrate a stopping rule for sampling different outputs from the LM that get added to a growing set of candidates until we are confident that the output set is sufficient. Since some samples may be low-quality, we also simultaneously calibrate and apply a rejection rule for removing candidates from the output set to reduce noise. Similar to conformal prediction, we prove that the sampled set returned by our procedure contains at least one acceptable answer with high probability, while still being empirically precise (i.e., small) on average. Furthermore, within this set of candidate responses, we show that we can also accurately identify subsets of individual components -- such as phrases or sentences -- that are each independently correct (e.g., that are not "hallucinations"), again with statistical guarantees. We demonstrate the promise of our approach on multiple tasks in open-domain question answering, text summarization, and radiology report generation using different LM variants

The effect of long-term Aronia melanocarpa extract supplementation on cognitive performance, mood, and vascular function: A randomized controlled trial in healthy, middle-aged individuals

Cognitive decline is associated with lifestyle-related factors such as overweight, blood pressure, and dietary composition. Studies have reported beneficial effects of dietary anthocyanins on cognition in older adults and children. However, the effect of anthocyanin-rich Aronia melanocarpa extract (AME) on cognition is unknown. Therefore, this study aimed to determine the effect of long-term supplementation with AME on cognitive performance, mood, and vascular function in healthy, middle-aged, overweight adults. In a randomized double-blind placebo-controlled parallel study, 101 participants either consumed 90 mg AME, 150 mg AME, or placebo for 24 weeks. The grooved pegboard test, number cross-out test, and Stroop test were performed as measures for psychomotor speed, attention, and cognitive flexibility. Mood was evaluated with a visual analogue scale, serum brain-derived neurotrophic factor (BDNF) was determined, and vascular function was assessed by carotid ultrasounds and blood pressure measurements. AME improved psychomotor speed compared to placebo (90 mg AME: change = -3.37; p = 0.009). Furthermore, 150 mg AME decreased brachial diastolic blood pressure compared to 90 mg AME (change = 2.44; p = 0.011), but not compared to placebo. Attention, cognitive flexibility, BDNF, and other vascular parameters were not affected. In conclusion, AME supplementation showed an indication of beneficial effects on cognitive performance and blood pressure in individuals at risk of cognitive decline

A Metric Framework for quantifying Data Concentration

Poor performance of artificial neural nets when applied to credit-related classification problems is investigated and contrasted with logistic regression classification. We propose that artificial neural nets are less successful because of the inherent structure of credit data rather than any particular aspect of the neural net structure. Three metrics are developed to rationalise the result with such data. The metrics exploit the distributional properties of the data to rationalise neural net results. They are used in conjunction with a variant of an established concentration measure that differentiates between class characteristics. The results are contrasted with those obtained using random data, and are compared with results obtained using logistic regression. We find, in general agreement with previous studies, that logistic regressions out-perform neural nets in the majority of cases. An approximate decision criterion is developed in order to explain adverse results

Machine Learning Methods for Image-based Personalized Cancer Screening

While AI has the potential to transform patient care, the development of equitable clinical AI models and their translation to hospitals remains difficult. From a computational perspective, these tools must deliver consistent performance across diverse populations and adapt to diverse clinical needs, while learning from biased and scarce data. Moreover, the development of tools relies on our capacity to balance clinical AI utility and patient privacy concerns. In this thesis, I will discuss our contributions in addressing the above challenges in three areas: 1) cancer risk assessment from imaging, 2) personalized screening policy design and 3) private data sharing through neural obfuscation. I have demonstrated that our clinical models offer significant improvements over the current standard of care across globally diverse patient populations. The models now underlie prospective clinical trails.Ph.D

Improving information extraction by acquiring external evidence with reinforcement learning

Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 33-35).Most successful information extraction systems operate with access to a large collection of documents. In this work, we explore the task of acquiring and incorporating external evidence to improve extraction accuracy in domains where the amount of training data is scarce. This process entails issuing search queries, extraction from new sources and reconciliation of extracted values, which are repeated until sufficient evidence is collected. We approach the problem using a reinforcement learning framework where our model learns to select optimal actions based on contextual information. We employ a deep Q-network, trained to optimize a reward function that reflects extraction accuracy while penalizing extra effort. Our experiments on two databases - of shooting incidents, and food adulteration cases - demonstrate that our system significantly outperforms traditional extractors and a competitive meta-classifier baselineby Adam Yala.M. Eng

Machine learning to parse breast pathology reports in Chinese

© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Introduction: Large structured databases of pathology findings are valuable in deriving new clinical insights. However, they are labor intensive to create and generally require manual annotation. There has been some work in the bioinformatics community to support automating this work via machine learning in English. Our contribution is to provide an automated approach to construct such structured databases in Chinese, and to set the stage for extraction from other languages. Methods: We collected 2104 de-identified Chinese benign and malignant breast pathology reports from Hunan Cancer Hospital. Physicians with native Chinese proficiency reviewed the reports and annotated a variety of binary and numerical pathologic entities. After excluding 78 cases with a bilateral lesion in the same report, 1216 cases were used as a training set for the algorithm, which was then refined by 405 development cases. The Natural language processing algorithm was tested by using the remaining 405 cases to evaluate the machine learning outcome. The model was used to extract 13 binary entities and 8 numerical entities. Results: When compared to physicians with native Chinese proficiency, the model showed a per-entity accuracy from 91 to 100% for all common diagnoses on the test set. The overall accuracy of binary entities was 98% and of numerical entities was 95%. In a per-report evaluation for binary entities with more than 100 training cases, 85% of all the testing reports were completely correct and 11% had an error in 1 out of 22 entities. Conclusion: We have demonstrated that Chinese breast pathology reports can be automatically parsed into structured data using standard machine learning approaches. The results of our study demonstrate that techniques effective in parsing English reports can be scaled to other languages