179 research outputs found
TableLlama: Towards Open Large Generalist Models for Tables
Semi-structured tables are ubiquitous. There has been a variety of tasks that
aim to automatically interpret, augment, and query tables. Current methods
often require pretraining on tables or special model architecture design, are
restricted to specific table types, or have simplifying assumptions about
tables and tasks. This paper makes the first step towards developing
open-source large language models (LLMs) as generalists for a diversity of
table-based tasks. Towards that end, we construct TableInstruct, a new dataset
with a variety of realistic tables and tasks, for instruction tuning and
evaluating LLMs. We further develop the first open-source generalist model for
tables, TableLlama, by fine-tuning Llama 2 (7B) with LongLoRA to address the
long context challenge. We experiment under both in-domain setting and
out-of-domain setting. On 7 out of 8 in-domain tasks, TableLlama achieves
comparable or better performance than the SOTA for each task, despite the
latter often has task-specific design. On 6 out-of-domain datasets, it achieves
6-48 absolute point gains compared with the base model, showing that training
on TableInstruct enhances the model's generalizability. We will open-source our
dataset and trained model to boost future work on developing open generalist
models for tables
AttributionBench: How Hard is Automatic Attribution Evaluation?
Modern generative search engines enhance the reliability of large language
model (LLM) responses by providing cited evidence. However, evaluating the
answer's attribution, i.e., whether every claim within the generated responses
is fully supported by its cited evidence, remains an open problem. This
verification, traditionally dependent on costly human evaluation, underscores
the urgent need for automatic attribution evaluation methods. To bridge the gap
in the absence of standardized benchmarks for these methods, we present
AttributionBench, a comprehensive benchmark compiled from various existing
attribution datasets. Our extensive experiments on AttributionBench reveal the
challenges of automatic attribution evaluation, even for state-of-the-art LLMs.
Specifically, our findings show that even a fine-tuned GPT-3.5 only achieves
around 80% macro-F1 under a binary classification formulation. A detailed
analysis of more than 300 error cases indicates that a majority of failures
stem from the model's inability to process nuanced information, and the
discrepancy between the information the model has access to and that human
annotators do
Rosmarinic Acid Prevents Cisplatin-Induced Liver and Kidney Injury by Inhibiting Inflammatory Responses and Enhancing Total Antioxidant Capacity, Thereby Activating the Nrf2 Signaling Pathway
Drug-induced liver and kidney damage is an emergent clinical issue that should be addressed. Rosmarinic acid (RA) has obvious anti-inflammatory and antioxidant effects, so we evaluated the anti-inflammatory and antioxidant effects of RA pretreatment on serum and liver and kidney tissues of cisplatin (CP)-treated mice and explored the possible mechanisms. The results showed that RA pretreatment effectively downregulated the serum, liver, and kidney levels of ALT, AST, BUN, and CRE and the inflammatory factors IL-1β, IL-6, and TNF-α, and simultaneously enhanced the total antioxidant capacity of the liver and kidney. RA pretreatment significantly reduced the levels of MPO, MDA, and NO in liver and kidney tissue, inhibited the mRNA expression of IL-1β, IL-6, and TNF-α in liver and kidney tissue, activated the Nrf2 signaling pathway, and upregulated the mRNA expression of downstream target genes. Our findings show that RA could effectively prevent and alleviate acute liver and kidney injury caused by CP
Acceleration compensation of a novel piezoelectric balance for the short duration impulse measurement: a time series analysis approach
A novel piezoelectric balance was developed to measure the six-component forces for the complex aircraft scaled model in the impulse combustion wind tunnel at a short duration airloads Mach number of 5. The piezoelectric balance using four triaxial piezoelectric load cells yields the high stiffness, sensitive and good dynamic response characteristics. The dynamic model-balance system was built to analyze the vibration characteristic. The time series analysis approach was developed on the basis of the system transfer function and the natural frequency, and the accelerated forces which induce the airloads overshooting oscillations had been obtained by the second order derivatives function. The experimental results have shown that the problem of overshooting oscillations effect of the impulse can be effectively solved by the acceleration compensation technology for the complex test model with the novel piezoelectric balance
Fully distributed consensus for high-order strict-feedback nonlinear multiagent systems with switched topologies
summary:This paper studies the distributed consensus problem of high-order strict-feedback nonlinear multiagent systems. By employing the adaptive backstepping technique and switched system theory, a novel protocol is proposed for MASs with switched topologies. Global information such as the number of agents and communication topology is not used. In addition, the communication topology between agents can be switched between possible topologies at any time. Based on the Lyapunov function method, the proposed adaptive protocol guarantees the complete consensus of multiagent systems without restricting the dwell time of the switched signal. Finally, two numerical examples are provided to illustrate the effectiveness and advantages of the given protocol
Label Propagation for Graph Label Noise
Label noise is a common challenge in large datasets, as it can significantly
degrade the generalization ability of deep neural networks. Most existing
studies focus on noisy labels in computer vision; however, graph models
encompass both node features and graph topology as input, and become more
susceptible to label noise through message-passing mechanisms. Recently, only a
few works have been proposed to tackle the label noise on graphs. One major
limitation is that they assume the graph is homophilous and the labels are
smoothly distributed. Nevertheless, real-world graphs may contain varying
degrees of heterophily or even be heterophily-dominated, leading to the
inadequacy of current methods. In this paper, we study graph label noise in the
context of arbitrary heterophily, with the aim of rectifying noisy labels and
assigning labels to previously unlabeled nodes. We begin by conducting two
empirical analyses to explore the impact of graph homophily on graph label
noise. Following observations, we propose a simple yet efficient algorithm,
denoted as LP4GLN. Specifically, LP4GLN is an iterative algorithm with three
steps: (1) reconstruct the graph to recover the homophily property, (2) utilize
label propagation to rectify the noisy labels, (3) select high-confidence
labels to retain for the next iteration. By iterating these steps, we obtain a
set of correct labels, ultimately achieving high accuracy in the node
classification task. The theoretical analysis is also provided to demonstrate
its remarkable denoising "effect". Finally, we conduct experiments on 10
benchmark datasets under varying graph heterophily levels and noise types,
comparing the performance of LP4GLN with 7 typical baselines. Our results
illustrate the superior performance of the proposed LP4GLN
Generating by Understanding: Neural Visual Generation with Logical Symbol Groundings
Despite the great success of neural visual generative models in recent years,
integrating them with strong symbolic knowledge reasoning systems remains a
challenging task. The main challenges are two-fold: one is symbol assignment,
i.e. bonding latent factors of neural visual generators with meaningful symbols
from knowledge reasoning systems. Another is rule learning, i.e. learning new
rules, which govern the generative process of the data, to augment the
knowledge reasoning systems. To deal with these symbol grounding problems, we
propose a neural-symbolic learning approach, Abductive Visual Generation
(AbdGen), for integrating logic programming systems with neural visual
generative models based on the abductive learning framework. To achieve
reliable and efficient symbol assignment, the quantized abduction method is
introduced for generating abduction proposals by the nearest-neighbor lookups
within semantic codebooks. To achieve precise rule learning, the contrastive
meta-abduction method is proposed to eliminate wrong rules with positive cases
and avoid less-informative rules with negative cases simultaneously.
Experimental results on various benchmark datasets show that compared to the
baselines, AbdGen requires significantly fewer instance-level labeling
information for symbol assignment. Furthermore, our approach can effectively
learn underlying logical generative rules from data, which is out of the
capability of existing approaches
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