7 research outputs found
LeanContext: Cost-Efficient Domain-Specific Question Answering Using LLMs
Question-answering (QA) is a significant application of Large Language Models
(LLMs), shaping chatbot capabilities across healthcare, education, and customer
service. However, widespread LLM integration presents a challenge for small
businesses due to the high expenses of LLM API usage. Costs rise rapidly when
domain-specific data (context) is used alongside queries for accurate
domain-specific LLM responses. One option is to summarize the context by using
LLMs and reduce the context. However, this can also filter out useful
information that is necessary to answer some domain-specific queries. In this
paper, we shift from human-oriented summarizers to AI model-friendly summaries.
Our approach, LeanContext, efficiently extracts key sentences from the
context that are closely aligned with the query. The choice of is neither
static nor random; we introduce a reinforcement learning technique that
dynamically determines based on the query and context. The rest of the less
important sentences are reduced using a free open source text reduction method.
We evaluate LeanContext against several recent query-aware and query-unaware
context reduction approaches on prominent datasets (arxiv papers and BBC news
articles). Despite cost reductions of to , LeanContext's
ROUGE-1 score decreases only by to compared to a baseline
that retains the entire context (no summarization). Additionally, if free
pretrained LLM-based summarizers are used to reduce context (into human
consumable summaries), LeanContext can further modify the reduced context to
enhance the accuracy (ROUGE-1 score) by to .Comment: The paper is under revie
Prediction of lung function in adolescence using epigenetic aging: A machine learning approach
Epigenetic aging has been found to be associated with a number of phenotypes and diseases. A few studies have investigated its effect on lung function in relatively older people. However, this effect has not been explored in the younger population. This study examines whether lung function in adolescence can be predicted with epigenetic age accelerations (AAs) using machine learning techniques. DNA methylation based AAs were estimated in 326 matched samples at two time points (at 10 years and 18 years) from the Isle of Wight Birth Cohort. Five machine learning regression models (linear, lasso, ridge, elastic net, and Bayesian ridge) were used to predict FEV1 (forced expiratory volume in one second) and FVC (forced vital capacity) at 18 years from feature selected predictor variables (based on mutual information) and AA changes between the two time points. The best models were ridge regression (R2 = 75.21% ± 7.42%; RMSE = 0.3768 ± 0.0653) and elastic net regression (R2 = 75.38% ± 6.98%; RMSE = 0.445 ± 0.069) for FEV1 and FVC, respectively. This study suggests that the application of machine learning in conjunction with tracking changes in AA over the life span can be beneficial to assess the lung health in adolescenc
MetaMorphosis: Task-oriented Privacy Cognizant Feature Generation for Multi-task Learning
With the growth of computer vision applications, deep learning, and edge
computing contribute to ensuring practical collaborative intelligence (CI) by
distributing the workload among edge devices and the cloud. However, running
separate single-task models on edge devices is inefficient regarding the
required computational resource and time. In this context, multi-task learning
allows leveraging a single deep learning model for performing multiple tasks,
such as semantic segmentation and depth estimation on incoming video frames.
This single processing pipeline generates common deep features that are shared
among multi-task modules. However, in a collaborative intelligence scenario,
generating common deep features has two major issues. First, the deep features
may inadvertently contain input information exposed to the downstream modules
(violating input privacy). Second, the generated universal features expose a
piece of collective information than what is intended for a certain task, in
which features for one task can be utilized to perform another task (violating
task privacy). This paper proposes a novel deep learning-based
privacy-cognizant feature generation process called MetaMorphosis that limits
inference capability to specific tasks at hand. To achieve this, we propose a
channel squeeze-excitation based feature metamorphosis module, Cross-SEC, to
achieve distinct attention of all tasks and a de-correlation loss function with
differential-privacy to train a deep learning model that produces distinct
privacy-aware features as an output for the respective tasks. With extensive
experimentation on four datasets consisting of diverse images related to scene
understanding and facial attributes, we show that MetaMorphosis outperforms
recent adversarial learning and universal feature generation methods by
guaranteeing privacy requirements in an efficient way for image and video
analytics.Comment: Preprint version, 22 pages. Keywords: Multi-task learning, neural
networks, collaborative intelligence, differential privacy, task privac
Prediction of lung Ffunction in adolescence using epigenetic aging: A Machine learning approach
Epigenetic aging has been found to be associated with a number of phenotypes and diseases. A few studies have investigated its effect on lung function in relatively older people. However, this effect has not been explored in the younger population. This study examines whether lung function in adolescence can be predicted with epigenetic age accelerations (AAs) using machine learning techniques. DNA methylation based AAs were estimated in 326 matched samples at two time points (at 10 years and 18 years) from the Isle of Wight Birth Cohort. Five machine learning regression models (linear, lasso, ridge, elastic net, and Bayesian ridge) were used to predict FEV1 (forced expiratory volume in one second) and FVC (forced vital capacity) at 18 years from feature selected predictor variables (based on mutual information) and AA changes between the two time points. The best models were ridge regression (R2 = 75.21% ± 7.42%; RMSE = 0.3768 ± 0.0653) and elastic net regression (R2 = 75.38% ± 6.98%; RMSE = 0.445 ± 0.069) for FEV1 and FVC, respectively. This study suggests that the application of machine learning in conjunction with tracking changes in AA over the life span can be beneficial to assess the lung health in adolescence.</p