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Found 13 papers, 6 papers with code
How Diversely Can Language Models Solve Problems? Exploring the Algorithmic Diversity of Model-Generated Code
In this work, we highlight the diversity of code generated by LMs as a critical criterion for evaluating their code generation capabilities.
Eliciting Instruction-tuned Code Language Models' Capabilities to Utilize Auxiliary Function for Code Generation
Our experimental results show the effectiveness of combining the base models' auxiliary function utilization ability with the instruction following ability.
Is Functional Correctness Enough to Evaluate Code Language Models? Exploring Diversity of Generated Codes
Language models (LMs) have exhibited impressive abilities in generating codes from natural language requirements.
Exploring Language Model's Code Generation Ability with Auxiliary Functions
However, our analysis also reveals the model's underutilized behavior to call the auxiliary function, suggesting the future direction to enhance their implementation by eliciting the auxiliary function call ability encoded in the models.
KoDialogBench: Evaluating Conversational Understanding of Language Models with Korean Dialogue Benchmark
As language models are often deployed as chatbot assistants, it becomes a virtue for models to engage in conversations in a user's first language.
Learning Topology-Specific Experts for Molecular Property Prediction
In this paper, motivated by this observation, we propose TopExpert to leverage topology-specific prediction models (referred to as experts), each of which is responsible for each molecular group sharing similar topological semantics.
Topic Taxonomy Expansion via Hierarchy-Aware Topic Phrase Generation
Topic taxonomies display hierarchical topic structures of a text corpus and provide topical knowledge to enhance various NLP applications.
Toward Interpretable Semantic Textual Similarity via Optimal Transport-based Contrastive Sentence Learning
In the end, we propose CLRCMD, a contrastive learning framework that optimizes RCMD of sentence pairs, which enhances the quality of sentence similarity and their interpretation.
Learnable Structural Semantic Readout for Graph Classification
By the help of a global readout operation that simply aggregates all node (or node-cluster) representations, existing GNN classifiers obtain a graph-level representation of an input graph and predict its class label using the representation.
OoMMix: Out-of-manifold Regularization in Contextual Embedding Space for Text Classification
Recent studies on neural networks with pre-trained weights (i. e., BERT) have mainly focused on a low-dimensional subspace, where the embedding vectors computed from input words (or their contexts) are located.
Out-of-Manifold Regularization in Contextual Embedding Space for Text Classification
Recent studies on neural networks with pre-trained weights (i. e., BERT) have mainly focused on a low-dimensional subspace, where the embedding vectors computed from input words (or their contexts) are located.
Learnable Dynamic Temporal Pooling for Time Series Classification
With the increase of available time series data, predicting their class labels has been one of the most important challenges in a wide range of disciplines.
BHIN2vec: Balancing the Type of Relation in Heterogeneous Information Network
We view the heterogeneous network embedding as simultaneously solving multiple tasks in which each task corresponds to each relation type in a network.
