Search Results for author: Yifan Hou

Found 12 papers, 10 papers with code

Towards a Mechanistic Interpretation of Multi-Step Reasoning Capabilities of Language Models

1 code implementation23 Oct 2023 Yifan Hou, Jiaoda Li, Yu Fei, Alessandro Stolfo, Wangchunshu Zhou, Guangtao Zeng, Antoine Bosselut, Mrinmaya Sachan

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.

Mitigating Label Biases for In-context Learning

1 code implementation28 May 2023 Yu Fei, Yifan Hou, Zeming Chen, Antoine Bosselut

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).

In-Context Learning text-classification +1

RecurrentGPT: Interactive Generation of (Arbitrarily) Long Text

2 code implementations22 May 2023 Wangchunshu Zhou, Yuchen Eleanor Jiang, Peng Cui, Tiannan Wang, Zhenxin Xiao, Yifan Hou, Ryan Cotterell, Mrinmaya Sachan

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.

Language Modelling Large Language Model

Adapters for Enhanced Modeling of Multilingual Knowledge and Text

1 code implementation24 Oct 2022 Yifan Hou, Wenxiang Jiao, Meizhen Liu, Carl Allen, Zhaopeng Tu, Mrinmaya Sachan

Specifically, we introduce a lightweight adapter set to enhance MLLMs with cross-lingual entity alignment and facts from MLKGs for many languages.

Entity Alignment

Mirror Complementary Transformer Network for RGB-thermal Salient Object Detection

1 code implementation7 Jul 2022 Xiurong Jiang, Lin Zhu, Yifan Hou, Hui Tian

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.

Autonomous Driving object-detection +3

A Representation Learning Framework for Property Graphs

1 code implementation Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2019 Yifan Hou, Hongzhi Chen, Changji Li, James Cheng, Ming-Chang Yang

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.

Graph Embedding Graph Representation Learning +3

What Has Been Enhanced in my Knowledge-Enhanced Language Model?

1 code implementation2 Feb 2022 Yifan Hou, Guoji Fu, Mrinmaya Sachan

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.

Graph Attention Language Modelling

Bird's Eye: Probing for Linguistic Graph Structures with a Simple Information-Theoretic Approach

1 code implementation ACL 2021 Yifan Hou, Mrinmaya Sachan

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

1 code implementation8 Jun 2020 Guoji Fu, Yifan Hou, Jian Zhang, Kaili Ma, Barakeel Fanseu Kamhoua, James Cheng

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.

Denoising Graph Attention +2

Context-Aware Online Learning for Course Recommendation of MOOC Big Data

no code implementations11 Oct 2016 Yifan Hou, Pan Zhou, Ting Wang, Li Yu, Yuchong Hu, Dapeng Wu

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.

Recommendation Systems

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