Ever: Mitigating Hallucination in Large Language Models through Real-Time Verification and Rectification

Large Language Models (LLMs) have demonstrated remarkable proficiency in generating fluent text. However, they often encounter the challenge of generating inaccurate or hallucinated content. This issue is common in both non-retrieval-based generation and retrieval-augmented generation approaches, and existing post-hoc rectification methods may not address the accumulated hallucination errors that may be caused by the "snowballing" issue, especially in reasoning tasks. To tackle these challenges, we introduce a novel approach called Real-time Verification and Rectification (Ever). Instead of waiting until the end of the generation process to rectify hallucinations, Ever employs a real-time, step-wise generation and hallucination rectification strategy. The primary objective is to detect and rectify hallucinations as they occur during the text generation process. When compared to both retrieval-based and non-retrieval-based baselines, Ever demonstrates a significant improvement in generating trustworthy and factually accurate text across a diverse range of tasks, including short-form QA, biography generation, and multi-hop reasoning

Extremal problems for disjoint graphs

For a simple graph $F$, let $\mathrm{EX}(n, F)$ and $\mathrm{EX_{sp}}(n,F)$ be the set of graphs with the maximum number of edges and the set of graphs with the maximum spectral radius in an $n$-vertex graph without any copy of the graph $F$, respectively. Let $F$ be a graph with $\mathrm{ex}(n,F)=e(T_{n,r})+O(1)$. In this paper, we show that $\mathrm{EX_{sp}}(n,kF)\subseteq \mathrm{EX}(n,kF)$ for sufficiently large $n$. This generalizes a result of Wang, Kang and Xue [J. Comb. Theory, Ser. B, 159(2023) 20-41]. We also determine the extremal graphs of $kF$ in term of the extremal graphs of $F$.Comment: 23 pages. arXiv admin note: text overlap with arXiv:2306.1674

Numerical study on steel-concrete composite floor systems under corner column removal scenario

[EN] This paper evaluates the robustness of steel-concrete composite floor systems subjected to Corner Column (CC) removal scenario based on numerical simulations. Firstly, a FE model is statically analysed subjected to a CC removal scenario, yielding the static load-displacement curve, the failure mode and load-transfer mechanisms. These results are compared with those of composite floor systems under an Internal Column (IC) removal scenario. Besides, the FE model was dynamically analysed by six times under the respective six levels of loads by suddenly removing the corner column. The dynamic displacement-time responses under all levels of loads were obtained. Six pairs of load versus peak displacement constitute the pseudo-static response, to assess the load-carrying capacity and ductility of this composite floor system subjected to a sudden corner-column-removal scenario. Lastly, dynamic increase factors (DIFs) are obtained through comparing the quasi-static and pseudo-static responses, which is further compared with DIF under IC scenario.Fu, QN.; Tan, KH. (2018). Numerical study on steel-concrete composite floor systems under corner column removal scenario. En Proceedings of the 12th International Conference on Advances in Steel-Concrete Composite Structures. ASCCS 2018. Editorial Universitat Politècnica de València. 897-904. https://doi.org/10.4995/ASCCS2018.2018.6941OCS89790

Noise-Tolerant Deep Neighborhood Embedding for Remotely Sensed Images With Label Noise

Recently, many deep learning-based methods have been developed for solving remote sensing (RS) scene classification or retrieval tasks. Most of the adopted loss functions for training these models require accurate annotations. However, the presence of noise in such annotations (also known as label noise) cannot be avoided in large-scale RS benchmark archives, resulting from geo-location/registration errors, land-cover changes, and diverse knowledge background of annotators. To overcome the influence of noisy labels on the learning process of deep models, we propose a new loss function called noise-tolerant deep neighborhood embedding which can accurately encode the semantic relationships among RS scenes. Specifically, we target at maximizing the leave-one-out K-NN score for uncovering the inherent neighborhood structure among the images in feature space. Moreover, we down-weight the contribution of potential noisy images by learning their localized structure and pruning the images with low leave-one-out K-NN scores. Based on our newly proposed loss function, classwise features can be more robustly discriminated. Our experiments, conducted on two benchmark RS datasets, validate the effectiveness of the proposed approach on three different RS scene interpretation tasks, including classification, clustering, and retrieval. The codes of this article will be publicly available from https://github.com/jiankang1991