Knowledge Graph (KG)-augmented Large Language Models (LLMs) have recently propelled significant advances in complex reasoning tasks, thanks to their broad domain knowledge and contextual awareness. Unfortunately, current methods often assume KGs to be complete, which is impractical given the inherent limitations of KG construction and the potential loss of contextual cues when converting unstructured text into entity-relation triples. In response, this paper proposes the Triple Context Restoration and Query-driven Feedback (TCR-QF) framework, which reconstructs the textual context underlying each triple to mitigate information loss, while dynamically refining the KG structure by iteratively incorporating query-relevant missing knowledge. Experiments on five benchmark question-answering datasets substantiate the effectiveness of TCR-QF in KG and LLM integration, where itachieves a 29.1% improvement in Exact Match and a 15.5% improvement in F1 over its state-of-the-art GraphRAG competitors.

The black-box nature of large language models (LLMs) necessitates the development of eXplainable AI (XAI) techniques for transparency and trustworthiness. However, evaluating these techniques remains a challenge. This study presents a general evaluation framework using four key metrics: Human-reasoning Agreement (HA), Robustness, Consistency, and Contrastivity. We assess the effectiveness of six explainability techniques from five different XAI categories model simplification (LIME), perturbation-based methods (SHAP), gradient-based approaches (InputXGradient, Grad-CAM), Layer-wise Relevance Propagation (LRP), and attention mechanisms-based explainability methods (Attention Mechanism Visualization, AMV) across five encoder-based language models: TinyBERT, BERTbase, BERTlarge, XLM-R large, and DeBERTa-xlarge, using the IMDB Movie Reviews and Tweet Sentiment Extraction (TSE) datasets. Our findings show that the model simplification-based XAI method (LIME) consistently outperforms across multiple metrics and models, significantly excelling in HA with a score of 0.9685 on DeBERTa-xlarge, robustness, and consistency as the complexity of large language models increases. AMV demonstrates the best Robustness, with scores as low as 0.0020. It also excels in Consistency, achieving near-perfect scores of 0.9999 across all models. Regarding Contrastivity, LRP performs the best, particularly on more complex models, with scores up to 0.9371.

Retrieval-Augmented Generation (RAG) models have drawn considerable attention in modern open-domain question answering. The effectiveness of RAG depends on the quality of the top retrieved documents. However, conventional retrieval methods sometimes fail to rank the most relevant documents at the top. In this paper, we introduce ASRank, a new re-ranking method based on scoring retrieved documents using zero-shot answer scent which relies on a pre-trained large language model to compute the likelihood of the document-derived answers aligning with the answer scent. Our approach demonstrates marked improvements across several datasets, including NQ, TriviaQA, WebQA, ArchivalQA, HotpotQA, and Entity Questions. Notably, ASRank increases Top-1 retrieval accuracy on NQ from $19.2\%$ to $46.5\%$ for MSS and $22.1\%$ to $47.3\%$ for BM25. It also shows strong retrieval performance on several datasets compared to state-of-the-art methods (47.3 Top-1 by ASRank vs 35.4 by UPR by BM25).

This article contains spoilers for Star Trek: Section 31. When last we saw our Star Trek: Discovery antihero--Her Most Imperial Majesty, Mother of the Fatherland, Overlord of Vulcan, Dominus of Qo'noS, Regina Andor, Philippa Georgiou Augustus Iaponius Centarius--back in 2020, she had just come through a particularly rough stretch. Georgiou (if you're nasty, and she certainly is) had … well, for starters, she'd been dragged from the fascist "mirror" universe where she was queen into the "prime" one, and then catapulted 930 years into the future to stop an evil A.I. from wiping out all sentient life in the galaxy. Got that done, thankfully, though not without some sassy shenanigans--but all the travel turned out to be a bit taxing, on both Georgiou's mind and molecules, which were straining like a multiversal rubber band to return backward and across, causing weird flashbacks and a nasty case of the decorporealizing shivers. Luckily, a mysterious sentient hard drive known as "the Sphere" that had been hanging out on her ship, the mushroom-fueled USS Discovery, was able to help locate a solution: a stout little man dressed in tweed and a bowler hat named Carl who was also, ahem, the "Guardian of Forever."

Andriy Kovalenko, head of Ukraine's centre for countering disinformation, said on Telegram that an oil refinery in Ryazan had been hit, as well as the Kremniy factory in Bryansk that Kyiv says produces missile components and other weapons. Bloggers on Telegram posted images and videos of fires raging at the Ryazan facility, which covers around 6sq km (2.3sq miles). Verified footage shows people fleeing from the site in cars and on foot as a fireball rises into the sky. BBC Verify used video footage to establish the location of two fires at the refinery. One video shows a fire near the northern entrance, whose location was matched by the road layout, signs and fences.

"The Agency" star Katherine Waterston admitted she finds AI generally "terrifying" for Hollywood and beyond. Screenwriter Paul Schrader, known for his critically acclaimed works like "Taxi Driver," "Raging Bull" and "First Reformed," surprised fans when he shared his apparent approval of artificial intelligence. In a series of posts last week, the Oscar-nominee marveled at AI and ChatGPT's capabilities when it came to his profession. "I've just come to realize AI is smarter than I am. Has better ideas, has more efficient ways to execute them," he wrote on Jan 16. "Taxi Driver" screenwriter and director Paul Schrader surprised fans with his interest in artificial intelligence.

This research addresses the challenge of camera calibration and distortion parameter prediction from a single image using deep learning models. The main contributions of this work are: (1) demonstrating that a deep learning model, trained on a mix of real and synthetic images, can accurately predict camera and lens parameters from a single image, and (2) developing a comprehensive synthetic dataset using the AILiveSim simulation platform. This dataset includes variations in focal length and lens distortion parameters, providing a robust foundation for model training and testing. The training process predominantly relied on these synthetic images, complemented by a small subset of real images, to explore how well models trained on synthetic data can perform calibration tasks on real-world images. Traditional calibration methods require multiple images of a calibration object from various orientations, which is often not feasible due to the lack of such images in publicly available datasets. A deep learning network based on the ResNet architecture was trained on this synthetic dataset to predict camera calibration parameters following the Brown-Conrady lens model. The ResNet architecture, adapted for regression tasks, is capable of predicting continuous values essential for accurate camera calibration in applications such as autonomous driving, robotics, and augmented reality. Keywords: Camera calibration, distortion, synthetic data, deep learning, residual networks (ResNet), AILiveSim, horizontal field-of-view, principal point, Brown-Conrady Model.

Text summarization involves reducing extensive documents to short sentences that encapsulate the essential ideas. The goal is to create a summary that effectively conveys the main points of the original text. We spend a significant amount of time each day reading the newspaper to stay informed about current events both domestically and internationally. While reading newspapers enriches our knowledge, we sometimes come across unnecessary content that isn't particularly relevant to our lives. In this paper, we introduce a neural network model designed to summarize Bangla text into concise and straightforward paragraphs, aiming for greater stability and efficiency.

While large generative artificial intelligence (GenAI) models have achieved significant success, they also raise growing concerns about online information security due to their potential misuse for generating deceptive content. Out-of-context (OOC) multimodal misinformation detection, which often retrieves Web evidence to identify the repurposing of images in false contexts, faces the issue of reasoning over GenAI-polluted evidence to derive accurate predictions. Existing works simulate GenAI-powered pollution at the claim level with stylistic rewriting to conceal linguistic cues, and ignore evidence-level pollution for such information-seeking applications. In this work, we investigate how polluted evidence affects the performance of existing OOC detectors, revealing a performance degradation of more than 9 percentage points. We propose two strategies, cross-modal evidence reranking and cross-modal claim-evidence reasoning, to address the challenges posed by polluted evidence. Extensive experiments on two benchmark datasets show that these strategies can effectively enhance the robustness of existing out-of-context detectors amidst polluted evidence.

A common approach to selecting examples at The use of in-context learning (ICL) with large inference-time is to generate embeddings of candidate language models (LLMs) has become a popular examples using a model like Sentence-BERT approach to achieve impressive performance in (Reimers, 2019) and retrieve the top-k most similar many NLP tasks (Raffel et al., 2020; Radford et al., examples for a given test instance, ranking them 2019). In ICL, models are prompted during inference based on distance or similarity. However, there is with task-specific examples that help condition a distinction between ranking examples (determining the generated output. Unlike fine-tuning, it how relevant they are to our test case) does not require updates to the model parameters, and ordering them (deciding how to arrange which offers many benefits with ever-increasing them in the prompt).