Search Results for author:
Found 15 papers, 1 papers with code
To What Extent Do Natural Language Understanding Datasets Correlate to Logical Reasoning? A Method for Diagnosing Logical Reasoning.
In this paper, we propose a systematic method to diagnose the correlations between an NLU dataset and a specific skill, and then take a fundamental reasoning skill, logical reasoning, as an example for analysis.
Exploring Logically Dependent Multi-task Learning with Causal Inference
In the field of causal inference, GS in our model is essentially a counterfactual reasoning process, trying to estimate the causal effect between tasks and utilize it to improve MTL.
Diagnosing the First-Order Logical Reasoning Ability Through LogicNLI
Recently, language models (LMs) have achieved significant performance on many NLU tasks, which has spurred widespread interest for their possible applications in the scientific and social area.
Look Before You Leap: Problem Elaboration Prompting Improves Mathematical Reasoning in Large Language Models
Large language models (LLMs) still grapple with complex tasks like mathematical reasoning.
Modeling Complex Mathematical Reasoning via Large Language Model based MathAgent
Large language models (LLMs) face challenges in solving complex mathematical problems that require comprehensive capacities to parse the statements, associate domain knowledge, perform compound logical reasoning, and integrate the intermediate rationales.
Comparable Demonstrations are Important in In-Context Learning: A Novel Perspective on Demonstration Selection
In-Context Learning (ICL) is an important paradigm for adapting Large Language Models (LLMs) to downstream tasks through a few demonstrations.
Chain-of-Thought Tuning: Masked Language Models can also Think Step By Step in Natural Language Understanding
From the perspective of CoT, CoTT's two-step framework enables MLMs to implement task decomposition; CoTT's prompt tuning allows intermediate steps to be used in natural language form.
Accurate Use of Label Dependency in Multi-Label Text Classification Through the Lens of Causality
In this study, we attribute the bias to the model's misuse of label dependency, i. e., the model tends to utilize the correlation shortcut in label dependency rather than fusing text information and label dependency for prediction.
Unlock the Potential of Counterfactually-Augmented Data in Out-Of-Distribution Generalization
Counterfactually-Augmented Data (CAD) -- minimal editing of sentences to flip the corresponding labels -- has the potential to improve the Out-Of-Distribution (OOD) generalization capability of language models, as CAD induces language models to exploit domain-independent causal features and exclude spurious correlations.
MaxGNR: A Dynamic Weight Strategy via Maximizing Gradient-to-Noise Ratio for Multi-Task Learning
A series of studies point out that too much gradient noise would lead to performance degradation in STL, however, in the MTL scenario, Inter-Task Gradient Noise (ITGN) is an additional source of gradient noise for each task, which can also affect the optimization process.
Improving the Out-Of-Distribution Generalization Capability of Language Models: Counterfactually-Augmented Data is not Enough
Counterfactually-Augmented Data (CAD) has the potential to improve language models' Out-Of-Distribution (OOD) generalization capability, as CAD induces language models to exploit causal features and exclude spurious correlations.
De-Confounded Variational Encoder-Decoder for Logical Table-to-Text Generation
The task remains challenging where deep learning models often generated linguistically fluent but logically inconsistent text.
Dependent Multi-Task Learning with Causal Intervention for Image Captioning
The intermediate task would help the model better understand the visual features and thus alleviate the content inconsistency problem.
A Semantically Consistent and Syntactically Variational Encoder-Decoder Framework for Paraphrase Generation
In this paper, we propose a semantically consistent and syntactically variational encoder-decoder framework, which uses adversarial learning to ensure the syntactic latent variable be semantic-free.
Show, Attend and Translate: Unpaired Multi-Domain Image-to-Image Translation with Visual Attention
Recently unpaired multi-domain image-to-image translation has attracted great interests and obtained remarkable progress, where a label vector is utilized to indicate multi-domain information.
