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Found 15 papers, 12 papers with code
Are Machines Better at Complex Reasoning? Unveiling Human-Machine Inference Gaps in Entailment Verification
Finally, we use this model to filter out inconsistent model-generated rationales in self-consistency decoding, resulting in a 6% accuracy improvement on average across three MCQ datasets.
Self-Contradictory Reasoning Evaluation and Detection
In a plethora of recent work, large language models (LLMs) demonstrated impressive reasoning ability, but many proposed downstream reasoning tasks focus on performance-wise evaluation.
PlaSma: Making Small Language Models Better Procedural Knowledge Models for (Counterfactual) Planning
In addition, we introduce a novel task, Counterfactual Planning, that requires a revision of a plan to cope with a counterfactual situation.
Faith and Fate: Limits of Transformers on Compositionality
We formulate compositional tasks as computation graphs to systematically quantify the level of complexity, and break down reasoning steps into intermediate sub-procedures.
APOLLO: A Simple Approach for Adaptive Pretraining of Language Models for Logical Reasoning
Logical reasoning of text is an important ability that requires understanding the information present in the text, their interconnections, and then reasoning through them to infer new conclusions.
Generate rather than Retrieve: Large Language Models are Strong Context Generators
We call our method generate-then-read (GenRead), which first prompts a large language model to generate contextutal documents based on a given question, and then reads the generated documents to produce the final answer.
RobustLR: Evaluating Robustness to Logical Perturbation in Deductive Reasoning
Transformers have been shown to be able to perform deductive reasoning on a logical rulebase containing rules and statements written in English natural language.
FaiRR: Faithful and Robust Deductive Reasoning over Natural Language
Recent works show that such models can also produce the reasoning steps (i. e., the proof graph) that emulate the model's logical reasoning process.
Discretized Integrated Gradients for Explaining Language Models
As a prominent attribution-based explanation algorithm, Integrated Gradients (IG) is widely adopted due to its desirable explanation axioms and the ease of gradient computation.
SalKG: Learning From Knowledge Graph Explanations for Commonsense Reasoning
and fine (Which nodes/paths in the KG are useful?)
ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations
Graph Neural Networks (GNN) have been shown to work effectively for modeling graph structured data to solve tasks such as node classification, link prediction and graph classification.
Ranked #3 on
Graph Classification
on FRANKENSTEIN
A Re-evaluation of Knowledge Graph Completion Methods
Knowledge Graph Completion (KGC) aims at automatically predicting missing links for large-scale knowledge graphs.
Ranked #25 on
Link Prediction
on FB15k-237
(MR metric)
Composition-based Multi-Relational Graph Convolutional Networks
Multi-relational graphs are a more general and prevalent form of graphs where each edge has a label and direction associated with it.
Ranked #22 on
Link Prediction
on FB15k-237
InteractE: Improving Convolution-based Knowledge Graph Embeddings by Increasing Feature Interactions
In this paper, we analyze how increasing the number of these interactions affects link prediction performance, and utilize our observations to propose InteractE.
Ranked #11 on
Link Prediction
on YAGO3-10
MT-CGCNN: Integrating Crystal Graph Convolutional Neural Network with Multitask Learning for Material Property Prediction
Some of the major challenges involved in developing such models are, (i) limited availability of materials data as compared to other fields, (ii) lack of universal descriptor of materials to predict its various properties.
Ranked #4 on
Band Gap
on Materials Project
