Search Results for author:
Found 29 papers, 21 papers with code
Neuro-Inspired Information-Theoretic Hierarchical Perception for Multimodal Learning
Integrating and processing information from various sources or modalities are critical for obtaining a comprehensive and accurate perception of the real world in autonomous systems and cyber-physical systems.
Complex Reasoning over Logical Queries on Commonsense Knowledge Graphs
Event commonsense reasoning requires the ability to reason about the relationship between events, as well as infer implicit context underlying that relationship.
AbsInstruct: Eliciting Abstraction Ability from LLMs through Explanation Tuning with Plausibility Estimation
Abstraction ability is crucial in human intelligence, which can also benefit various tasks in NLP study.
EntailE: Introducing Textual Entailment in Commonsense Knowledge Graph Completion
In this paper, we propose to adopt textual entailment to find implicit entailment relations between CSKG nodes, to effectively densify the subgraph connecting nodes within the same conceptual class, which indicates a similar level of plausibility.
ConstraintChecker: A Plugin for Large Language Models to Reason on Commonsense Knowledge Bases
When considering a new knowledge instance, ConstraintChecker employs a rule-based module to produce a list of constraints, then it uses a zero-shot learning module to check whether this knowledge instance satisfies all constraints.
CANDLE: Iterative Conceptualization and Instantiation Distillation from Large Language Models for Commonsense Reasoning
The sequential process of conceptualization and instantiation is essential to generalizable commonsense reasoning as it allows the application of existing knowledge to unfamiliar scenarios.
AbsPyramid: Benchmarking the Abstraction Ability of Language Models with a Unified Entailment Graph
Cognitive research indicates that abstraction ability is essential in human intelligence, which remains under-explored in language models.
StoryAnalogy: Deriving Story-level Analogies from Large Language Models to Unlock Analogical Understanding
Analogy-making between narratives is crucial for human reasoning.
QADYNAMICS: Training Dynamics-Driven Synthetic QA Diagnostic for Zero-Shot Commonsense Question Answering
Zero-shot commonsense Question-Answering (QA) requires models to reason about general situations beyond specific benchmarks.
KCTS: Knowledge-Constrained Tree Search Decoding with Token-Level Hallucination Detection
Large Language Models (LLMs) have demonstrated remarkable human-level natural language generation capabilities.
Neuro-Inspired Hierarchical Multimodal Learning
Integrating and processing information from various sources or modalities are critical for obtaining a comprehensive and accurate perception of the real world.
CommonsenseVIS: Visualizing and Understanding Commonsense Reasoning Capabilities of Natural Language Models
Specifically, we extract relevant commonsense knowledge in inputs as references to align model behavior with human knowledge.
Getting Sick After Seeing a Doctor? Diagnosing and Mitigating Knowledge Conflicts in Event Temporal Reasoning
However, knowledge conflicts arise when there is a mismatch between the actual temporal relations of events in the context and the prior knowledge or biases learned by the model.
CAR: Conceptualization-Augmented Reasoner for Zero-Shot Commonsense Question Answering
The task of zero-shot commonsense question answering evaluates models on their capacity to reason about general scenarios beyond those presented in specific datasets.
COLA: Contextualized Commonsense Causal Reasoning from the Causal Inference Perspective
This paper proposes a new task to detect commonsense causation between two events in an event sequence (i. e., context), called contextualized commonsense causal reasoning.
CAT: A Contextualized Conceptualization and Instantiation Framework for Commonsense Reasoning
Commonsense reasoning, aiming at endowing machines with a human-like ability to make situational presumptions, is extremely challenging to generalize.
ChatGPT Evaluation on Sentence Level Relations: A Focus on Temporal, Causal, and Discourse Relations
This paper aims to quantitatively evaluate the performance of ChatGPT, an interactive large language model, on inter-sentential relations such as temporal relations, causal relations, and discourse relations.
CKBP v2: An Expert-Annotated Evaluation Set for Commonsense Knowledge Base Population
Populating Commonsense Knowledge Bases (CSKB) is an important yet hard task in NLP, as it tackles knowledge from external sources with unseen events and entities.
On-the-fly Denoising for Data Augmentation in Natural Language Understanding
However, data augmentation may introduce noisy data that impairs training.
MICO: A Multi-alternative Contrastive Learning Framework for Commonsense Knowledge Representation
Commonsense reasoning tasks such as commonsense knowledge graph completion and commonsense question answering require powerful representation learning.
PseudoReasoner: Leveraging Pseudo Labels for Commonsense Knowledge Base Population
We propose PseudoReasoner, a semi-supervised learning framework for CSKB population that uses a teacher model pre-trained on CSKBs to provide pseudo labels on the unlabeled candidate dataset for a student model to learn from.
SubeventWriter: Iterative Sub-event Sequence Generation with Coherence Controller
In this paper, we propose a new task of sub-event generation for an unseen process to evaluate the understanding of the coherence of sub-event actions and objects.
Acquiring and Modelling Abstract Commonsense Knowledge via Conceptualization
Conceptualization, or viewing entities and situations as instances of abstract concepts in mind and making inferences based on that, is a vital component in human intelligence for commonsense reasoning.
Weakly Supervised Text Classification using Supervision Signals from a Language Model
In this paper, we propose to query a masked language model with cloze style prompts to obtain supervision signals.
Benchmarking Commonsense Knowledge Base Population with an Effective Evaluation Dataset
Experimental results show that generalizing commonsense reasoning on unseen assertions is inherently a hard task.
Probing Toxic Content in Large Pre-Trained Language Models
Large pre-trained language models (PTLMs) have been shown to carry biases towards different social groups which leads to the reproduction of stereotypical and toxic content by major NLP systems.
Do Boat and Ocean Suggest Beach? Dialogue Summarization with External Knowledge
To evaluate the inference capability of different methods, we also propose a new evaluation metric based on CODC.
ASER: Towards Large-scale Commonsense Knowledge Acquisition via Higher-order Selectional Preference over Eventualities
After conceptualization with Probase, a selectional preference based concept-instance relational knowledge base, our concept graph contains 15 million conceptualized eventualities and 224 million edges between them.
DISCOS: Bridging the Gap between Discourse Knowledge and Commonsense Knowledge
On the other hand, generation models have the potential to automatically generate more knowledge.
