A LLM (Large Language Model) is an artificial intelligence model characterized by its vast size in terms of parameters and training data. These models are typically based on deep learning architectures, such as Transformers, and are trained on extensive datasets to learn the statistical relationships and patterns within natural language.

Posts

TimeCAP: Learning to Contextualize, Augment, and Predict Time Series Events with Large Language Model Agents

/in /by

Time series data is essential in various applications, including climate modeling, healthcare monitoring, and financial analytics. Understanding the contextual information associated with real-world time series data is often essential for accurate and reliable event predictions. In this paper, we introduce TimeCAP, a time-series processing framework that creatively employs Large Language Models (LLMs) as contextualizers of time series data, extending their typical usage as predictors. TimeCAP incorporates two independent LLM agents: one generates a textual summary capturing the context of the time series, while the other uses this enriched summary to make more informed predictions. In addition, TimeCAP employs a multi-modal encoder that synergizes with the LLM agents, enhancing predictive performance through mutual augmentation of inputs with in-context examples. Experimental results on real-world datasets demonstrate that TimeCAP outperforms state-of-the-art methods for time series event prediction, including those utilizing LLMs as predictors, achieving an average improvement of 28.75% in F1 score.

Read more

NEC Labs America Attends the 39th Annual AAAI Conference on Artificial Intelligence #AAAI25

/in /by

Our NEC Lab America team attended the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25), in Philadelphia, Pennsylvania at the Pennsylvania Convention Center from February 25 to March 4, 2025. The purpose of the AAAI conference series was to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. Our team presented technical papers, led special tracks, delivered talks on key topics, participated in workshops, conducted tutorials, and showcased research in poster sessions. The team greeted visitors at Booth #208 and was there Thursday through Saturday.

Read more

RAG-check: Evaluating Multimodal Retrieval Augmented Generation Performance

/in /by

Retrieval-augmented generation (RAG) improves large language models (LLMs) by using external knowledge to guide response generation, reducing hallucinations. However, RAG, particularly multi-modal RAG, can introduce new hallucination sources: (i) the retrieval process may select irrelevant pieces (e.g., documents, images) as raw context from the database, and (ii) retrieved images are processed into text-based context via vision-language models (VLMs) or directly used by multi-modal language models (MLLMs) like GPT-4o, which may hallucinate. To address this, we propose a novel framework to evaluate the reliability of multi-modal RAG using two performance measures: (i) the relevancy score (RS), assessing the relevance of retrieved entries to the query, and (ii) the correctness score (CS), evaluating the accuracy of the generated response. We train RS and CS models using a ChatGPT-derived database and human evaluator samples. Results show that both models achieve ~88% accuracy on test data. Additionally, we construct a 5000-sample human-annotated database evaluating the relevancy of retrieved pieces and the correctness of response statements. Our RS model aligns with human preferences 20% more often than CLIP in retrieval, and our CS model matches human preferences ~91% of the time. Finally, we assess various RAG systems’ selection and generation performances using RS and CS.

Read more

Re-ranking the Context for Multimodal Retrieval Augmented Generation

/in /by

Retrieval-augmented generation (RAG) enhances large language models (LLMs) by incorporating external knowledge to generate a response within a context with improved accuracy and reduced hallucinations. However, multi-modal RAG systems face unique challenges: (i) the retrieval process may select irrelevant entries to user query (e.g., images, documents), and (ii) vision-language models or multi-modal language models like GPT-4o may hallucinate when processing these entries to generate RAG output. In this paper, we aim to address the first challenge, i.e, improving the selection of relevant context from the knowledge-base in retrieval phase of the multi-modal RAG. Specifically, we leverage the relevancy score (RS) measure designed in our previous work for evaluating the RAG performance to select more relevant entries in retrieval process. The retrieval based on embeddings, say CLIP-based embedding, and cosine similarity usually perform poorly particularly for multi-modal data. We show that by using a more advanced relevancy measure, one can enhance the retrieval process by selecting more relevant pieces from the knowledge-base and eliminate the irrelevant pieces from the context by adaptively selecting up-to-k entries instead of fixed number of entries. Our evaluation using COCO dataset demonstrates significant enhancement in selecting relevant context and accuracy of the generated response.

Read more

Multi-hop Evidence Pursuit Meets the Web: Team Papelo at FEVER 2024

/in /by

Separating disinformation from fact on the web has long challenged both the search and the reasoning powers of humans. We show that the reasoning power of large language models (LLMs) and the retrieval power of modern search engines can be combined to automate this process and explainably verify claims. We integrate LLMs and search under a multi-hop evidence pursuit strategy. This strategy generates an initial question based on an input claim using a sequence to sequence model, searches and formulates an answer to the question, and iteratively generates follow-up questions to pursue the evidence that is missing using an LLM. We demonstrate our system on the FEVER 2024 (AVeriTeC) shared task. Compared to a strategy of generating all the questions at once, our method obtains .045 higher label accuracy and .155 higher AVeriTeC score (evaluating the adequacy of the evidence). Through ablations, we show the importance of various design choices, such as the question generation method, medium-sized context, reasoning with one document at a time, adding metadata, paraphrasing, reducing the problem to two classes, and reconsidering the final verdict. Our submitted system achieves .510 AVeriTeC score on the dev set and .477 AVeriTec score on the test set.

Read more

A Survey on Detection of LLMs-Generated Content

/in /by

The burgeoning capabilities of advanced large language models (LLMs) such as ChatGPT have led to an increase in synthetic content generation with implications across a variety of sectors, including media, cybersecurity, public discourse, and education. As such, the ability to detect LLMs-generated content has become of paramount importance. We aim to provide a detailed overview of existing detection strategies and benchmarks, scrutinizing their differences and identifying key challenges and prospects in the field, advocating for more adaptable and robust models to enhance detection accuracy. We also posit the necessity for a multi-faceted approach to defend against various attacks to counter the rapidly advancing capabilities of LLMs. To the best of our knowledge, this work is the first comprehensive survey on the detection in the era of LLMs. We hope it will provide a broad understanding of the current landscape of LLMs-generated content detection, and we have maintained a website to consistently update the latest research as a guiding reference for researchers and practitioners.

Read more

Large Language Models Can Be Contextual Privacy Protection Learners

/in /by

The proliferation of Large Language Models (LLMs) has driven considerable interest in fine-tuning them with domain-specific data to create specialized language models. Nevertheless, such domain-specific fine-tuning data often contains contextually sensitive personally identifiable information (PII). Direct fine-tuning LLMs on this data without privacy protection poses a risk of data leakage of sensitive PII during inference time. To address this challenge, we introduce Contextual Privacy Protection Language Models (CPPLM), a novel paradigm for fine-tuning LLMs that effectively injects domain-specific knowledge while safeguarding inference-time data privacy. Our work offers a theoretical analysis for model design and delves into various techniques such as corpus curation, penalty-based unlikelihood in training loss, and instruction-based tuning, etc. Extensive experiments across diverse datasets and scenarios demonstrate the effectiveness of our approaches. In particular, instruction tuning with both positive and negative examples, stands out as a promising method, effectively protecting private data while enhancing the model s knowledge. Our work underscores the potential for Large Language Models as robust contextual privacy protection learners.

Read more

DiCE: Distributed Code generation and Execution

/in /by

Generative artificial intelligence (GenAI), specifically, Large Language Models (LLMs), have shown tremendous potential in automating several tasks and improving human productivity. Recent works have shown them to be quite useful in writing and summarizing text (articles, blogs, poems, stories, songs, etc.), answering questions, brainstorming ideas, and even writing code. Several LLMs have emerged specifically targeting code generation. Given a prompt, these LLMs can generate code in any desired programming language. Many tools like ChatGPT, CoPilot, CodeWhisperer, Cody, DeepSeek Coder, StarCoder, etc. are now routinely being used by software developers. However, most of the prior work in automatic code generation using LLMs is focused on obtaining “correct” and working code, and mainly runs on a single computer (serial code). In this paper, we take this to the next level, where LLMs are leveraged to generate code for execution on a distributed infrastructure. We propose a novel system called DiCE, which takes serial code as input and automatically generates distributed version of the code and efficiently executes it on a distributed setup. DiCE consists of two main components (a) LLM-based tool (Synthia) to understand dependencies in serial code and automatically generate distributed version of the code using specialized programming model and semantics, and (b) Runtime (Hermod) to understand the semantics in the distributed code and realize efficient execution on a cluster of machines (distributed infrastructure). DiCE currently focuses on visual programs synthesized by tools like ViperGPT [1] and VisReP [2] (serial code), automatically identifies higher-level task parallelism opportunities (e.g., parallel object detection), transforms the code to exploit the parallelism, and finally efficiently executes it on a cluster of machines. Through our experiments using 100 examples from the GQA dataset [3], we show that the serial codes generated by ViperGPT are successfully transformed into distributed codes which are then efficiently executed on a cluster of machines by DiCE. We note that DiCE correctly identifies opportunities for parallelism and distributes tasks on separate GPUs within the cluster. We observe an average speed-up of 2X, 2.95X, and 3.7X, and an average efficiency of 1, 0.74 and 0.48 for a cluster of 2 nodes, 4 nodes, and 8 nodes, respectively.

Read more

Accelerating Distributed Machine Learning with an Efficient AllReduce Routing Strategy

/in /by

We propose an efficient routing strategy for AllReduce transfers, which compromise of the dominant traffic in machine learning-centric datacenters, to achieve fast parameter synchronization in distributed machine learning, improving the average training time by 9%.

Read more

Introducing Our New Project: Time Series Language Model for Explainable AI

/in /by

Our new project, Time Series Language Model for Explainable AI, represents a significant leap forward in the field of forecasting and explainable AI. By combining advanced forecasting techniques with explainable AI, we are paving the way for a future where data-driven insights are not only accurate but also comprehensible and actionable.

Read more