GNPy Experimental Validation in a C+L Multiband Optical Multiplex Section

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The GNPy quality-of-transmission estimator has undergone improvements and rigorous experimental validation in a C+L multiband transmission scenario. This includes the incorporation of a disaggregated generalized Gaussian noise model, along with advanced modeling of amplifiers and transceivers. The recently proposed implementation demonstrates notable enhancements, offering highly accurate GSNR predictions on commercial C+L-band equipment while significantly reducing computation time.

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Optical Amplified Line Self-Healing Using GNPy as a Service by the SDN Control

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A control architecture for a partially disaggregated optical network is proposed using a GNPy-based digital twin for QoT estimation. The proposed implementation enables soft failure mitigation by autonomously adjusting the amplifier working points.

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Knowledge-enhanced Prompt Learning for Open-domain Commonsense Reasoning

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Neural language models for commonsense reasoning often formulate the problem as a QA task and make predictions based on learned representations of language after fine-tuning. However, without providing any fine-tuning data and pre-defined answer candidates, can neural language models still answer commonsense reasoning questions only relying on external knowledge? In this work, we investigate a unique yet challenging problem-open-domain commonsense reasoning that aims to answer questions without providing any answer candidates and fine-tuning examples. A team comprising NECLA (NEC Laboratories America) and NEC Digital Business Platform Unit proposed method leverages neural language models to iteratively retrieve reasoning chains on the external knowledge base, which does not require task-specific supervision. The reasoning chains can help to identify the most precise answer to the commonsense question and its corresponding knowledge statements to justify the answer choice. This technology has proven its effectiveness in a diverse array of business domains.

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Optimizing LLM API usage costs with novel query-aware reduction of relevant enterprise data

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Costs of LLM API usage rise rapidly when proprietary enterprise data is used as context for user queries to generate more accurate responses from LLMs. To reduce costs, we propose LeanContext, which generates query-aware, compact and AI model-friendly summaries of relevant enterprise data context. This is unlike traditional summarizers that produce query-unaware human-friendly summaries that are also not as compact. We first use retrieval augmented generation (RAG) to generate a query-aware enterprise data context, which includes key, query-relevant enterprise data. Then, we use reinforcement learning to further reduce the context while ensuring that a prompt consisting of the user query and the reduced context elicits an LLM response that is just as accurate as the LLM response to a prompt that uses the original enterprise data context. Our reduced context is not only query-dependent, but it is also variable-sized. Our experimental results demonstrate that LeanContext (a) reduces costs of LLM API usage by 37% to 68% (compared to RAG), while maintaining the accuracy of the LLM response, and (b) improves accuracy of responses by 26% to 38% when state-of-the-art summarizers reduce RAG context.

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Foundational Vision-LLM for AI Linkage and Orchestration

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We propose a vision-LLM framework for automating development and deployment of computer vision solutions for pre-defined or custom-defined tasks. A foundational layer is proposed with a code-LLM AI orchestrator self-trained with reinforcement learning to create Python code based on its understanding of a novel user-defined task, together with APIs, documentation and usage notes of existing task-specific AI models. Zero-shot abilities in specific domains are obtained through foundational vision-language models trained at a low compute expense leveraging existing computer vision models and datasets. An engine layer is proposed which comprises of several task-specific vision-language engines which can be compositionally utilized. An application-specific layer is proposed to improve performance in customer-specific scenarios, using novel LLM-guided data augmentation and question decomposition, besides standard fine-tuning tools. We demonstrate a range of applications including visual AI assistance, visual conversation, law enforcement, mobility, medical image reasoning and remote sensing.

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LLMs and MI Bring Innovation to Material Development Platforms

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In this paper, we introduce efforts to apply large language models (LLMs) to the field of material development. NEC is advancing the development of a material development platform. By applying core technologies corresponding to two material development steps, namely investigation activities (Read paper/patent) and experimental planning (Design Experiment Plan), the platform organizes documents such as papers and reports as well as data such as experimental results and then presents in an interactive way to users. In addition, with techniquesthat reflect physical and chemical principles into machine learning models, AI can learn even with limited data and accurately predict material properties. Through this platform, we aim to achieve the seamless integration of materials informatics (MI) with a vast body of industry literature and knowledge, thereby bringing innovation to the material development process.

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Pruning as a Domain-specific LLM Extractor

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Large Language Models (LLMs) have exhibited remarkable proficiency across a wide array of NLP tasks. However, the escalation in model size also engenders substantial deployment costs. While few efforts have explored model pruning techniques to reduce the size of LLMs, they mainly center on general or task-specific weights. This leads to suboptimal performance due to lacking specificity on the target domain or generality on different tasks when applied to domain-specific challenges. This work introduces an innovative unstructured dual-pruning methodology, D-PRUNER, for domain-specific compression on LLM. It extracts a compressed, domain-specific, and task agnostic LLM by identifying LLM weights that are pivotal for general capabilities, like linguistic capability and multi-task solving, and domain-specific knowledge. More specifically, we first assess general weight importance by quantifying the error incurred upon their removal with the help of an open-domain calibration dataset. Then, we utilize this general weight importance to refine the training loss, so that it preserves generality when fitting into a specific domain. Moreover, by efficiently approximating weight importance with the refined training loss on a domain-specific calibration dataset, we obtain a pruned model emphasizing generality and specificity. Our comprehensive experiments across various tasks in healthcare and legal domains show the effectiveness of D-PRUNER in domain-specific compression. Our code is available at https: //github.com/psunlpgroup/D-Pruner.

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Uncertainty Quantification for In-Context Learning of Large Language Models

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In-context learning has emerged as a groundbreaking ability of Large Language Models (LLMs) and revolutionized various fields by providing a few task-relevant demonstrations in the prompt. However, trustworthy issues with LLM’s response, such as hallucination, have also been actively discussed. Existing works have been devoted to quantifying the uncertainty in LLM’s response, but they often overlook the complex nature of LLMs and the uniqueness of in-context learning. In this work, we delve into the predictive uncertainty of LLMs associated with in-context learning, highlighting that such uncertainties may stem from both the provided demonstrations (aleatoric uncertainty) and ambiguities tied to the model’s configurations (epistemic uncertainty). We propose a novel formulation and corresponding estimation method to quantify both types of uncertainties. The proposed method offers an unsupervised way to understand the prediction of in-context learning in a plug-and-play fashion. Extensive experiments are conducted to demonstrate the effectiveness of the decomposition. The code and data are available at: https://github.com/lingchen0331/UQ_ICL.

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Weakly-Supervised Temporal Action Localization with Multi-Modal Plateau Transformers

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Weakly Supervised Temporal Action Localization (WSTAL) aims to jointly localize and classify action segments in untrimmed videos with only video level annotations. To leverage video level annotations most existing methods adopt the multiple instance learning paradigm where frame/snippet level action predictions are first produced and then aggregated to form a video-level prediction. Although there are trials to improve snippet-level predictions by modeling temporal relationships we argue that those implementations have not sufficiently exploited such information. In this paper we propose Multi Modal Plateau Transformers (M2PT) for WSTAL by simultaneously exploiting temporal relationships among snippets complementary information across data modalities and temporal coherence among consecutive snippets. Specifically M2PT explores a dual Transformer architecture for RGB and optical flow modalities which models intra modality temporal relationship with a self attention mechanism and inter modality temporal relationship with a cross attention mechanism. To capture the temporal coherence that consecutive snippets are supposed to be assigned with the same action M2PT deploys a Plateau model to refine the temporal localization of action segments. Experimental results on popular benchmarks demonstrate that our proposed M2PT achieves state of the art performance.

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Taming Self-Training for Open-Vocabulary Object Detection

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Recent studies have shown promising performance in open-vocabulary object detection (OVD) by utilizing pseudo labels (PLs) from pretrained vision and language models (VLMs). However, teacher-student self-training, a powerful and widely used paradigm to leverage PLs, is rarely explored for OVD.

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