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Found 25 papers, 13 papers with code
Flexible Generation from Fragmentary Linguistic Input
We hypothesize that human performance is better characterized by flexible inference through composition of basic computational motifs available to the human language user.
Clean-image Backdoor Attacks
To gather a significant quantity of annotated training data for high-performance image classification models, numerous companies opt to enlist third-party providers to label their unlabeled data.
On the Tip of the Tongue: Analyzing Conceptual Representation in Large Language Models with Reverse-Dictionary Probe
Here we re-purpose the reverse dictionary task as a case study to probe LLMs' capacity for conceptual inference.
Demystifying Bitcoin Address Behavior via Graph Neural Networks
Finally, we aggregate all graph embeddings of an address into the address-level representation, and engage in a classification model to give the address behavior classification.
When Does Syntax Mediate Neural Language Model Performance? Evidence from Dropout Probes
Recent causal probing literature reveals when language models and syntactic probes use similar representations.
Controlled Evaluation of Grammatical Knowledge in Mandarin Chinese Language Models
We find suggestive evidence that structural supervision helps with representing syntactic state across intervening content and improves performance in low-data settings, suggesting that the benefits of hierarchical inductive biases in acquiring dependency relationships may extend beyond English.
Structural Guidance for Transformer Language Models
Here we study whether structural guidance leads to more human-like systematic linguistic generalization in Transformer language models without resorting to pre-training on very large amounts of data.
Combining Graph Neural Networks with Expert Knowledge for Smart Contract Vulnerability Detection
Then, we propose a novel temporal message propagation network to extract the graph feature from the normalized graph, and combine the graph feature with designed expert patterns to yield a final detection system.
Smart Contract Vulnerability Detection: From Pure Neural Network to Interpretable Graph Feature and Expert Pattern Fusion
In this paper, we explore combining deep learning with expert patterns in an explainable fashion.
What if This Modified That? Syntactic Interventions via Counterfactual Embeddings
Neural language models exhibit impressive performance on a variety of tasks, but their internal reasoning may be difficult to understand.
Digital Quantum Simulation of Floquet Topological Phases with a Solid-State Quantum Simulator
Quantum simulator with the ability to harness the dynamics of complex quantum systems has emerged as a promising platform for probing exotic topological phases.
Quantum Physics
Structural Supervision Improves Few-Shot Learning and Syntactic Generalization in Neural Language Models
Humans can learn structural properties about a word from minimal experience, and deploy their learned syntactic representations uniformly in different grammatical contexts.
SyntaxGym: An Online Platform for Targeted Evaluation of Language Models
Targeted syntactic evaluations have yielded insights into the generalizations learned by neural network language models.
On the Predictive Power of Neural Language Models for Human Real-Time Comprehension Behavior
Human reading behavior is tuned to the statistics of natural language: the time it takes human subjects to read a word can be predicted from estimates of the word's probability in context.
A Systematic Assessment of Syntactic Generalization in Neural Language Models
While state-of-the-art neural network models continue to achieve lower perplexity scores on language modeling benchmarks, it remains unknown whether optimizing for broad-coverage predictive performance leads to human-like syntactic knowledge.
Representation of Constituents in Neural Language Models: Coordination Phrase as a Case Study
We assess whether different neural language models trained on English and French represent phrase-level number and gender features, and use those features to drive downstream expectations.
Neural Language Models as Psycholinguistic Subjects: Representations of Syntactic State
We deploy the methods of controlled psycholinguistic experimentation to shed light on the extent to which the behavior of neural network language models reflects incremental representations of syntactic state.
Structural Supervision Improves Learning of Non-Local Grammatical Dependencies
State-of-the-art LSTM language models trained on large corpora learn sequential contingencies in impressive detail and have been shown to acquire a number of non-local grammatical dependencies with some success.
Bridging LSTM Architecture and the Neural Dynamics during Reading
Recently, the long short-term memory neural network (LSTM) has attracted wide interest due to its success in many tasks.
Overview of the NLPCC 2015 Shared Task: Chinese Word Segmentation and POS Tagging for Micro-blog Texts
In this paper, we give an overview for the shared task at the 4th CCF Conference on Natural Language Processing \& Chinese Computing (NLPCC 2015): Chinese word segmentation and part-of-speech (POS) tagging for micro-blog texts.
