Md Adnan Arefeen works at University of Missouri-Kansas City.

Posts

iRAG: An Incremental Retrieval Augmented Generation System for Videos

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Retrieval augmented generation (RAG) systems combine the strengths of language generation and information retrieval to power many real-world applications like chatbots. Use of RAG for combined understanding of multimodal data such as text, images and videos is appealing but two critical limitations exist: one-time, upfront capture of all content in large multimodal data as text descriptions entails high processing times, and not all information in the rich multimodal data is typically in the text descriptions. Since the user queries are not known apriori, developing a system for multimodal to text conversion and interactive querying of multimodal data is challenging.To address these limitations, we propose iRAG, which augments RAG with a novel incremental workflow to enable interactive querying of large corpus of multimodal data. Unlike traditional RAG, iRAG quickly indexes large repositories of multimodal data, and in the incremental workflow, it uses the index to opportunistically extract more details from select portions of the multimodal data to retrieve context relevant to an interactive user query. Such an incremental workflow avoids long multimodal to text conversion times, overcomes information loss issues by doing on-demand query-specific extraction of details in multimodal data, and ensures high quality of responses to interactive user queries that are often not known apriori. To the best of our knowledge, iRAG is the first system to augment RAG with an incremental workflow to support efficient interactive querying of large, real-world multimodal data. Experimental results on real-world long videos demonstrate 23x to 25x faster video to text ingestion, while ensuring that quality of responses to interactive user queries is comparable to responses from a traditional RAG where all video data is converted to text upfront before any querying.

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LeanContext: Cost-Efficient Domain-Specific Question Answering using LLMs

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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 k key sentences from the context that are closely aligned with the query. The choice of k is neither static nor random; we introduce a reinforcement learning technique that dynamically determines k 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 37.29% to 67.81%, LeanContext’s ROUGE-1 score decreases only by 1.41% to 2.65% 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 13.22% to 24.61%.

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FactionFormer: Context-Driven Collaborative Vision Transformer Models for Edge Intelligence

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Edge Intelligence has received attention in the recent times for its potential towards improving responsiveness, reducing the cost of data transmission, enhancing security and privacy, and enabling autonomous decisions by edge devices. However, edge devices lack the power and compute resources necessary to execute most Al models. In this paper, we present FactionFormer, a novel method to deploy resource-intensive deep-learning models, such as vision transformers (ViT), on resource-constrained edge devices. Our method is based on a key observation: edge devices are often deployed in settings where they encounter only a subset of the classes that the resource intensive Al model is trained to classify, and this subset changes across deployments. Therefore, we automatically identify this subset as a faction, devise on-the fly a bespoke resource-efficient ViT called a modelette for the faction and set up an efficient processing pipeline consisting of a modelette on the device, a wireless network such as 5G, and the resource-intensive ViT model on an edge server, all of which work collaboratively to do the inference. For several ViT models pre-trained on benchmark datasets, FactionFormer’s modelettes are up to 4× smaller than the corresponding baseline models in terms of the number of parameters, and they can infer up to 2.5× faster than the baseline setup where every input is processed by the resource-intensive ViT on the edge server. Our work is the first of its kind to propose a device-edge collaborative inference framework where bespoke deep learning models for the device are automatically devised on-the-fly for most frequently encountered subset of classes.

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Chimera: Context-Aware Splittable Deep Multitasking Models for Edge Intelligence

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Design of multitasking deep learning models has mostly focused on improving the accuracy of the constituent tasks, but the challenges of efficiently deploying such models in a device-edge collaborative setup (that is common in 5G deployments) has not been investigated. Towards this end, in this paper, we propose an approach called Chimera 1 for training (done Offline) and deployment (done Online) of multitasking deep learning models that are splittable across the device and edge. In the offline phase, we train our multi-tasking setup such that features from a pre-trained model for one of the tasks (called the Primary task) are extracted and task-specific sub-models are trained to generate the other (Secondary) tasks’ outputs through a knowledge distillation like training strategy to mimic the outputs of pre-trained models for the tasks. The task-specific sub-models are designed to be significantly lightweight than the original pre-trained models for the Secondary tasks. Once the sub-models are trained, during deployment, for given deployment context, characterized by the configurations, we search for the optimal (in terms of both model performance and cost) deployment strategy for the generated multitasking model, through finding one or multiple suitable layer(s) for splitting the model, so that inference workloads are distributed between the device and the edge server and the inference is done in a collaborative manner. Extensive experiments on benchmark computer vision tasks demonstrate that Chimera generates splittable multitasking models that are at least ~ 3 x parameter efficient than the existing such models, and the end-to-end device-edge collaborative inference becomes ~ 1.35 x faster with our choice of context-aware splitting decisions.

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