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Found 14 papers, 2 papers with code
Self-Retrieval: Building an Information Retrieval System with One Large Language Model
The rise of large language models (LLMs) has transformed the role of information retrieval (IR) systems in the way to humans accessing information.
Unified Language Representation for Question Answering over Text, Tables, and Images
When trying to answer complex questions, people often rely on multiple sources of information, such as visual, textual, and tabular data.
Coarse-to-Fine Knowledge Selection for Document Grounded Dialogs
Specifically, the former efficiently finds relevant passages in a retrieval-and-reranking process, whereas the latter effectively extracts finer-grain spans within those passages to incorporate into a parametric answer generation model (BART, T5).
Metropolitan Segment Traffic Speeds from Massive Floating Car Data in 10 Cities
Traffic analysis is crucial for urban operations and planning, while the availability of dense urban traffic data beyond loop detectors is still scarce.
TripLe: Revisiting Pretrained Model Reuse and Progressive Learning for Efficient Vision Transformer Scaling and Searching
One promising way to accelerate transformer training is to reuse small pretrained models to initialize the transformer, as their existing representation power facilitates faster model convergence.
Layout-Aware Information Extraction for Document-Grounded Dialogue: Dataset, Method and Demonstration
In this paper, we propose a Layout-aware document-level Information Extraction dataset, LIE, to facilitate the study of extracting both structural and semantic knowledge from visually rich documents (VRDs), so as to generate accurate responses in dialogue systems.
Bridging the Gap between Reality and Ideality of Entity Matching: A Revisiting and Benchmark Re-Construction
Experimental results demonstrate that the assumptions made in the previous benchmark construction process are not coincidental with the open environment, which conceal the main challenges of the task and therefore significantly overestimate the current progress of entity matching.
ProFlip: Targeted Trojan Attack With Progressive Bit Flips
In this work, we present ProFlip, the first targeted Trojan attack framework that can divert the prediction of the DNN to the target class by progressively identifying and flipping a small set of bits in model parameters.
N-Bref : A High-fidelity Decompiler Exploiting Programming Structures
In software development, decompilation aims to reverse engineer binary executables.
Towards Measuring Place Function Similarity at Fine Spatial Granularity with Trajectory Embedding
The embedding similarity was previously proposed as a new metric for measuring the similarity of place functions.
Coda: An End-to-End Neural Program Decompiler
Furthermore, Coda outperforms the sequence-to-sequence model with attention by a margin of 70% program accuracy.
A Neural-based Program Decompiler
Reverse engineering of binary executables is a critical problem in the computer security domain.
Towards Fast and Energy-Efficient Binarized Neural Network Inference on FPGA
Thus there does exist redundancy that can be exploited to further reduce the amount of on-chip computations.
