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Found 15 papers, 5 papers with code
Themis: Towards Flexible and Interpretable NLG Evaluation
The evaluation of natural language generation (NLG) tasks is a significant and longstanding research issue.
Task Oriented In-Domain Data Augmentation
On average, TRAIT improves LLM performance by 8% in the advertisement domain and 7. 5% in the math domain.
MC-MKE: A Fine-Grained Multimodal Knowledge Editing Benchmark Emphasizing Modality Consistency
Our benchmark facilitates independent correction of misreading and misrecognition errors by editing the corresponding knowledge component.
Are LLM-based Evaluators Confusing NLG Quality Criteria?
Some prior work has shown that LLMs perform well in NLG evaluation for different tasks.
LLM-based NLG Evaluation: Current Status and Challenges
Evaluating natural language generation (NLG) is a vital but challenging problem in artificial intelligence.
Evoke: Evoking Critical Thinking Abilities in LLMs via Reviewer-Author Prompt Editing
In Evoke, there are two instances of a same LLM: one as a reviewer (LLM-Reviewer), it scores the current prompt; the other as an author (LLM-Author), it edits the prompt by considering the edit history and the reviewer's feedback.
Solving Inverse Problems with Latent Diffusion Models via Hard Data Consistency
However, training diffusion models in the pixel space are both data-intensive and computationally demanding, which restricts their applicability as priors for high-dimensional real-world data such as medical images.
DeepTagger: Knowledge Enhanced Named Entity Recognition for Web-Based Ads Queries
For model-free enhancement, we collect unlabeled web queries to augment domain knowledge; and we collect web search results to enrich the information of ads queries.
Error-Robust Retrieval for Chinese Spelling Check
Chinese Spelling Check (CSC) aims to detect and correct error tokens in Chinese contexts, which has a wide range of applications.
Implementation of improved RGBD 3D target detection model based on FPGA heterogeneous computing architecture
In order to solve the problems of low model accuracy, poor computing power, poor parallel ability and excessive power consumption in the deployment of RGBD based 3 D target detection model at the embedded end, this paper first proposes an improved RGBD 3 D target detection model based on ENet semantic segmentation model, which takes ENet as the semantic segmentation network, RGB image and depth information are fused to realize 3 D target detection. Secondly, in order to apply the model at the edge, this paper constructs a lightweight network and cuts the network in the down-sampling stage of ENet model. Finally, this paper uses Xilinx ZCU104 as the hardware development kit, which takes FPGA as the auxiliary parallel operation unit and ARM as the main operation unit. It is a heterogeneous computing architecture with the ability to deal with complex operations. The architecture uses FPGA to accelerate the depth model in parallel, which improves the operation speed and reduces the power consumption. The test results of the model on ZCU104 are compared with other hardware. The results show that while ensuring the accuracy, the power consumption of the heterogeneous computing architecture used in this paper is 93% lowerthan that of Intel Xeon e5-2620 v4 CPU, the speed is 12 times higher, and the speed is more than 180 times higher than that of ARM Cortex-A53 commonly used at the edge.
DeeprETA: An ETA Post-processing System at Scale
Estimated Time of Arrival (ETA) plays an important role in delivery and ride-hailing platforms.
5th Place Solution for VSPW 2021 Challenge
In this article, we introduce the solution we used in the VSPW 2021 Challenge.
A Survey of Knowledge Enhanced Pre-trained Models
Pre-trained language models learn informative word representations on a large-scale text corpus through self-supervised learning, which has achieved promising performance in fields of natural language processing (NLP) after fine-tuning.
When does loss-based prioritization fail?
Each example is propagated forward and backward through the network the same amount of times, independent of how much the example contributes to the learning protocol.
Applying SVGD to Bayesian Neural Networks for Cyclical Time-Series Prediction and Inference
A regression-based BNN model is proposed to predict spatiotemporal quantities like hourly rider demand with calibrated uncertainties.
