The recent emergence of Large Language Models based on the Transformer architecture has enabled dramatic advancements in the field of Natural Language Processing. However, these models have long inference latency, which limits their deployment and makes them prohibitively expensive for various real-time applications. The inference latency is further exacerbated by autoregressive generative tasks, as models need to run iteratively to generate tokens sequentially without leveraging token-level parallelization. To address this, we propose Big Little Decoder (BiLD), a framework that can improve inference efficiency and latency for a wide range of text generation applications. The BiLD framework contains two models with different sizes that collaboratively generate text.

AI has the potential to augment human decision making. However, even high-performing models can produce inaccurate predictions when deployed. These inaccuracies, combined with automation bias, where humans overrely on AI predictions, can result in worse decisions. Selective prediction, in which potentially unreliable model predictions are hidden from users, has been proposed as a solution. This approach assumes that when AI abstains and informs the user so, humans make decisions as they would without AI involvement. To test this assumption, we study the effects of selective prediction on human decisions in a clinical context. We conducted a user study of 259 clinicians tasked with diagnosing and treating hospitalized patients. We compared their baseline performance without any AI involvement to their AI-assisted accuracy with and without selective prediction. Our findings indicate that selective prediction mitigates the negative effects of inaccurate AI in terms of decision accuracy. Compared to no AI assistance, clinician accuracy declined when shown inaccurate AI predictions (66% [95% CI: 56%-75%] vs. 56% [95% CI: 46%-66%]), but recovered under selective prediction (64% [95% CI: 54%-73%]). However, while selective prediction nearly maintains overall accuracy, our results suggest that it alters patterns of mistakes: when informed the AI abstains, clinicians underdiagnose (18% increase in missed diagnoses) and undertreat (35% increase in missed treatments) compared to no AI input at all. Our findings underscore the importance of empirically validating assumptions about how humans engage with AI within human-AI systems.

The recent emergence of Large Language Models based on the Transformer architecture has enabled dramatic advancements in the field of Natural Language Processing. However, these models have long inference latency, which limits their deployment and makes them prohibitively expensive for various real-time applications. The inference latency is further exacerbated by autoregressive generative tasks, as models need to run iteratively to generate tokens sequentially without leveraging token-level parallelization. To address this, we propose Big Little Decoder (BiLD), a framework that can improve inference efficiency and latency for a wide range of text generation applications. The BiLD framework contains two models with different sizes that collaboratively generate text.

In Machine Learning, an error is an important measure of how accurately our model can predict on data that it uses to learn as well as how it behaves with new and unseen data. Based on the error, we tend to choose the machine learning model which would produce the best performance on a particular dataset. In this article, we will be discussing one such problem in machine learning models known as the Bias-Variance Tradeoff. We will try to explore what it is and how one can overcome it. Below is the outline of important points that we will cover in this article.

In Machine Learning, an error is an important measure of how accurately our model can predict on data that it uses to learn as well as how it behaves with new and unseen data. Based on the error, we tend to choose the machine learning model which would produce the best performance on a particular dataset. In this article, we will be discussing one such problem in machine learning models known as the Bias-Variance Tradeoff. We will try to explore what it is and how one can overcome it. Below is the outline of important points that we will cover in this article.

Despite a growing body of research focused on creating interpretable machine learning methods, there have been few empirical studies verifying whether interpretable methods achieve their intended effects on end users. We present a framework for assessing the effects of model interpretability on users via pre-registered experiments in which participants are shown functionally identical models that vary in factors thought to influence interpretability. Using this framework, we ran a sequence of large-scale randomized experiments, varying two putative drivers of interpretability: the number of features and the model transparency (clear or black-box). We measured how these factors impact trust in model predictions, the ability to simulate a model, and the ability to detect a model's mistakes. We found that participants who were shown a clear model with a small number of features were better able to simulate the model's predictions. However, we found no difference in multiple measures of trust and found that clear models did not improve the ability to correct mistakes. These findings suggest that interpretability research could benefit from more emphasis on empirically verifying that interpretable models achieve all their intended effects.

This is great, in the sense that you don't have to worry about accidently forgetting to carry the 1 or remember how each rule in calculus operates. It is still great to have a general understanding of some of the equations you can utilize, distributions you can model and general statistics rules that can help clean up your data! We need to quickly lay out some definitions. In this post we will talk about discrete variables. If you have not heard the term before this references variables that are of a limited set. It actually could include numbers that are decimals pending on the set of variables you are using. However, these rules need to be established. For instance, you can't have 3.5783123 medical procedures in real life.