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Found 12 papers, 10 papers with code
Towards a Mechanistic Interpretation of Multi-Step Reasoning Capabilities of Language Models
We show that MechanisticProbe is able to detect the information of the reasoning tree from the model's attentions for most examples, suggesting that the LM indeed is going through a process of multi-step reasoning within its architecture in many cases.
simPLE: a visuotactile method learned in simulation to precisely pick, localize, regrasp, and place objects
We propose simPLE (simulation to Pick Localize and PLacE) as a solution to precise pick-and-place.
Mitigating Label Biases for In-context Learning
In this work, we define a typology for three types of label biases in ICL for text classification: vanilla-label bias, context-label bias, and domain-label bias (which we conceptualize and detect for the first time).
RecurrentGPT: Interactive Generation of (Arbitrarily) Long Text
In addition to producing AI-generated content (AIGC), we also demonstrate the possibility of using RecurrentGPT as an interactive fiction that directly interacts with consumers.
Adapters for Enhanced Modeling of Multilingual Knowledge and Text
Specifically, we introduce a lightweight adapter set to enhance MLLMs with cross-lingual entity alignment and facts from MLKGs for many languages.
Mirror Complementary Transformer Network for RGB-thermal Salient Object Detection
Thus, the key problem of RGB-T SOD is to make the features from the two modalities complement and adjust each other flexibly, since it is inevitable that any modalities of RGB-T image pairs failure due to challenging scenes such as extreme light conditions and thermal crossover.
A Representation Learning Framework for Property Graphs
Representation learning on graphs, also called graph embedding, has demonstrated its significant impact on a series of machine learning applications such as classification, prediction and recommendation.
Measuring and Improving the Use of Graph Information in Graph Neural Networks
Graph neural networks (GNNs) have been widely used for representation learning on graph data.
What Has Been Enhanced in my Knowledge-Enhanced Language Model?
We conduct experiments to verify that our GCS can indeed be used to correctly interpret the KI process, and we use it to analyze two well-known knowledge-enhanced LMs: ERNIE and K-Adapter, and find that only a small amount of factual knowledge is integrated in them.
Bird's Eye: Probing for Linguistic Graph Structures with a Simple Information-Theoretic Approach
However, due to the inter-dependence of various phenomena and randomness of training probe models, detecting how these representations encode the rich information in these linguistic graphs remains a challenging problem.
Understanding Graph Neural Networks from Graph Signal Denoising Perspectives
This paper aims to provide a theoretical framework to understand GNNs, specifically, spectral graph convolutional networks and graph attention networks, from graph signal denoising perspectives.
Context-Aware Online Learning for Course Recommendation of MOOC Big Data
In this respect, the key challenge is how to realize personalized course recommendation as well as to reduce the computing and storage costs for the tremendous course data.
