Search Results for author:
Found 19 papers, 13 papers with code
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.
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.
Gold: A Global and Local-aware Denoising Framework for Commonsense Knowledge Graph Noise Detection
Commonsense Knowledge Graphs (CSKGs) are crucial for commonsense reasoning, yet constructing them through human annotations can be costly.
KCTS: Knowledge-Constrained Tree Search Decoding with Token-Level Hallucination Detection
Large Language Models (LLMs) have demonstrated remarkable human-level natural language generation capabilities.
TILFA: A Unified Framework for Text, Image, and Layout Fusion in Argument Mining
A main goal of Argument Mining (AM) is to analyze an author's stance.
Label-free Deep Learning Driven Secure Access Selection in Space-Air-Ground Integrated Networks
Considering the inherent co-channel interference due to spectrum sharing among multi-tier access networks in SAGIN, it can be leveraged to assist the physical layer security among heterogeneous transmissions.
A Unified Interactive Model Evaluation for Classification, Object Detection, and Instance Segmentation in Computer Vision
Existing model evaluation tools mainly focus on evaluating classification models, leaving a gap in evaluating more complex models, such as object detection.
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.
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.
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.
Legal Element-oriented Modeling with Multi-view Contrastive Learning for Legal Case Retrieval
In addition to general topical relevance, the relevant cases also involve similar situations and legal elements, which can support the judgment of the current case.
SeATrans: Learning Segmentation-Assisted diagnosis model via Transformer
To model the segmentation-diagnosis interaction, SeA-block first embeds the diagnosis feature based on the segmentation information via the encoder, and then transfers the embedding back to the diagnosis feature space by a decoder.
Learning self-calibrated optic disc and cup segmentation from multi-rater annotations
In this paper, we propose a novel neural network framework to learn OD/OC segmentation from multi-rater annotations.
Opinions Vary? Diagnosis First!
Motivated by the observation that OD/OC segmentation is often used for the glaucoma diagnosis clinically, in this paper, we propose a novel strategy to fuse the multi-rater OD/OC segmentation labels via the glaucoma diagnosis performance.
UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation
In this paper, we take one step further to propose an ultra-simplified formulation of GCNs (dubbed UltraGCN), which skips infinite layers of message passing for efficient recommendation.
Ranked #3 on
Recommendation Systems
on Gowalla
UNBERT: User-News Matching BERT for News Recommendation
Nowadays, news recommendation has become a popular channel for users to access news of their interests.
SparTerm: Learning Term-based Sparse Representation for Fast Text Retrieval
Term-based sparse representations dominate the first-stage text retrieval in industrial applications, due to its advantage in efficiency, interpretability, and exact term matching.
