175 research outputs found
Understanding Hidden Memories of Recurrent Neural Networks
Recurrent neural networks (RNNs) have been successfully applied to various
natural language processing (NLP) tasks and achieved better results than
conventional methods. However, the lack of understanding of the mechanisms
behind their effectiveness limits further improvements on their architectures.
In this paper, we present a visual analytics method for understanding and
comparing RNN models for NLP tasks. We propose a technique to explain the
function of individual hidden state units based on their expected response to
input texts. We then co-cluster hidden state units and words based on the
expected response and visualize co-clustering results as memory chips and word
clouds to provide more structured knowledge on RNNs' hidden states. We also
propose a glyph-based sequence visualization based on aggregate information to
analyze the behavior of an RNN's hidden state at the sentence-level. The
usability and effectiveness of our method are demonstrated through case studies
and reviews from domain experts.Comment: Published at IEEE Conference on Visual Analytics Science and
Technology (IEEE VAST 2017
Improving Fine-grained Entity Typing with Entity Linking
Fine-grained entity typing is a challenging problem since it usually involves
a relatively large tag set and may require to understand the context of the
entity mention. In this paper, we use entity linking to help with the
fine-grained entity type classification process. We propose a deep neural model
that makes predictions based on both the context and the information obtained
from entity linking results. Experimental results on two commonly used datasets
demonstrates the effectiveness of our approach. On both datasets, it achieves
more than 5\% absolute strict accuracy improvement over the state of the art.Comment: EMNLP 201
Peer-inspired student performance prediction in interactive online question pools with graph neural network
Student performance prediction is critical to online education. It can
benefit many downstream tasks on online learning platforms, such as estimating
dropout rates, facilitating strategic intervention, and enabling adaptive
online learning. Interactive online question pools provide students with
interesting interactive questions to practice their knowledge in online
education. However, little research has been done on student performance
prediction in interactive online question pools. Existing work on student
performance prediction targets at online learning platforms with predefined
course curriculum and accurate knowledge labels like MOOC platforms, but they
are not able to fully model knowledge evolution of students in interactive
online question pools. In this paper, we propose a novel approach using Graph
Neural Networks (GNNs) to achieve better student performance prediction in
interactive online question pools. Specifically, we model the relationship
between students and questions using student interactions to construct the
student-interaction-question network and further present a new GNN model,
called R^2GCN, which intrinsically works for the heterogeneous networks, to
achieve generalizable student performance prediction in interactive online
question pools. We evaluate the effectiveness of our approach on a real-world
dataset consisting of 104,113 mouse trajectories generated in the
problem-solving process of over 4000 students on 1631 questions. The experiment
results show that our approach can achieve a much higher accuracy of student
performance prediction than both traditional machine learning approaches and
GNN models.Comment: 8 pages, 8 figures. Accepted at CIKM 202
Multi-step Jailbreaking Privacy Attacks on ChatGPT
With the rapid progress of large language models (LLMs), many downstream NLP
tasks can be well solved given good prompts. Though model developers and
researchers work hard on dialog safety to avoid generating harmful content from
LLMs, it is still challenging to steer AI-generated content (AIGC) for the
human good. As powerful LLMs are devouring existing text data from various
domains (e.g., GPT-3 is trained on 45TB texts), it is natural to doubt whether
the private information is included in the training data and what privacy
threats can these LLMs and their downstream applications bring. In this paper,
we study the privacy threats from OpenAI's model APIs and New Bing enhanced by
ChatGPT and show that application-integrated LLMs may cause more severe privacy
threats ever than before. To this end, we conduct extensive experiments to
support our claims and discuss LLMs' privacy implications.Comment: Work in progres
Peer-inspired Student Performance Prediction in Interactive Online Question Pools with Graph Neural Network
Student performance prediction is critical to online education. It can
benefit many downstream tasks on online learning platforms, such as estimating
dropout rates, facilitating strategic intervention, and enabling adaptive
online learning. Interactive online question pools provide students with
interesting interactive questions to practice their knowledge in online
education. However, little research has been done on student performance
prediction in interactive online question pools. Existing work on student
performance prediction targets at online learning platforms with predefined
course curriculum and accurate knowledge labels like MOOC platforms, but they
are not able to fully model knowledge evolution of students in interactive
online question pools. In this paper, we propose a novel approach using Graph
Neural Networks (GNNs) to achieve better student performance prediction in
interactive online question pools. Specifically, we model the relationship
between students and questions using student interactions to construct the
student-interaction-question network and further present a new GNN model,
called R^2GCN, which intrinsically works for the heterogeneous networks, to
achieve generalizable student performance prediction in interactive online
question pools. We evaluate the effectiveness of our approach on a real-world
dataset consisting of 104,113 mouse trajectories generated in the
problem-solving process of over 4000 students on 1631 questions. The experiment
results show that our approach can achieve a much higher accuracy of student
performance prediction than both traditional machine learning approaches and
GNN models.Comment: 8 pages, 8 figures. Accepted at CIKM 202
Knowledge Graph Reasoning over Entities and Numerical Values
A complex logic query in a knowledge graph refers to a query expressed in
logic form that conveys a complex meaning, such as where did the Canadian
Turing award winner graduate from? Knowledge graph reasoning-based
applications, such as dialogue systems and interactive search engines, rely on
the ability to answer complex logic queries as a fundamental task. In most
knowledge graphs, edges are typically used to either describe the relationships
between entities or their associated attribute values. An attribute value can
be in categorical or numerical format, such as dates, years, sizes, etc.
However, existing complex query answering (CQA) methods simply treat numerical
values in the same way as they treat entities. This can lead to difficulties in
answering certain queries, such as which Australian Pulitzer award winner is
born before 1927, and which drug is a pain reliever and has fewer side effects
than Paracetamol. In this work, inspired by the recent advances in numerical
encoding and knowledge graph reasoning, we propose numerical complex query
answering. In this task, we introduce new numerical variables and operations to
describe queries involving numerical attribute values. To address the
difference between entities and numerical values, we also propose the framework
of Number Reasoning Network (NRN) for alternatively encoding entities and
numerical values into separate encoding structures. During the numerical
encoding process, NRN employs a parameterized density function to encode the
distribution of numerical values. During the entity encoding process, NRN uses
established query encoding methods for the original CQA problem. Experimental
results show that NRN consistently improves various query encoding methods on
three different knowledge graphs and achieves state-of-the-art results
Evolution of T-cell clonality in a patient with Ph-negative acute lymphocytic leukemia occurring after interferon and imatinib therapy for Ph-positive chronic myeloid leukemia
<p>Abstract</p> <p>Introduction</p> <p>The development of Philadelphia chromosome (Ph) negative acute leukemia/myelodysplastic syndrome (MDS) in patients with Ph-positive chronic myeloid leukemia (CML) is very rare. The features of restrictive usage and absence of partial T cell clones have been found in patients with CML. However, the T-cell clonal evolution of Ph-negative malignancies during treatment for CML is still unknown.</p> <p>Objective</p> <p>To investigate the dynamic change of clonal proliferation of T cell receptor (TCR) VĪ± and VĪ² subfamilies in one CML patient who developed Ph-negative acute lymphoblastic leukemia (ALL) after interferon and imatinib therapy.</p> <p>Methods</p> <p>The peripheral blood mononuclear cells (PBMC) samples were collected at the 3 time points (diagnosis of Ph-positive chronic phase (CP) CML, developing Ph-negative ALL and post inductive chemotherapy (CT) for Ph-negative ALL, respectively). The CDR3 size of TCR VĪ± and VĪ² repertoire were detected by RT-PCR. The PCR products were further analyzed by genescan to identify T cell clonality.</p> <p>Results</p> <p>The CML patient who achieved complete cytogenetic remission (CCR) after 5 years of IFN-Ī± therapy suddenly developed Ph-negative ALL 6 months following switch to imatinib therapy. The expression pattern and clonality of TCR VĪ±/VĪ² T cells changed in different disease stages. The restrictive expression of VĪ±/VĪ² subfamilies could be found in all three stages, and partial subfamily of T cells showed clonal proliferation. Additionally, there have been obvious differences in VĪ±/VĪ² subfamily of T cells between the stages of Ph-positive CML-CP and Ph-negative ALL. The VĪ±10 and VĪ²3 T cells evolved from oligoclonality to polyclonality, the VĪ²13 T cells changed from bioclonality to polyclonality, when Ph-negative ALL developed.</p> <p>Conclusions</p> <p>Restrictive usage and clonal proliferation of different VĪ±/VĪ² subfamily T cells between the stages of Ph-positive CP and Ph-negative ALL were detected in one patient. These changes may play a role in Ph- negative leukemogenesis.</p
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