Search Results for author:
Found 8 papers, 6 papers with code
On the Causal Nature of Sentiment Analysis
Sentiment analysis (SA) aims to identify the sentiment expressed in a text, such as a product review.
Predicting the Risk of Ischemic Stroke in Patients with Atrial Fibrillation using Heterogeneous Drug-protein-disease Network-based Deep Learning
The model integrates drug-protein-disease pathways and real-world clinical data of AF patients to generate the IS risk and potential pathways for each patient.
CLadder: Assessing Causal Reasoning in Language Models
Much of the existing work in natural language processing (NLP) focuses on evaluating commonsense causal reasoning in LLMs, thus failing to assess whether a model can perform causal inference in accordance with a set of well-defined formal rules.
Can Large Language Models Infer Causation from Correlation?
In this work, we propose the first benchmark dataset to test the pure causal inference skills of large language models (LLMs).
Voices of Her: Analyzing Gender Differences in the AI Publication World
While several previous studies have analyzed gender bias in research, we are still missing a comprehensive analysis of gender differences in the AI community, covering diverse topics and different development trends.
Psychologically-Inspired Causal Prompts
In this work, we take sentiment classification as an example and look into the causal relations between the review (X) and sentiment (Y).
Logical Fallacy Detection
In this paper, we propose the task of logical fallacy detection, and provide a new dataset (Logic) of logical fallacies generally found in text, together with an additional challenge set for detecting logical fallacies in climate change claims (LogicClimate).
Pre-training Language Model Incorporating Domain-specific Heterogeneous Knowledge into A Unified Representation
In this paper, we propose a heterogeneous knowledge language model (\textbf{HKLM}), a unified pre-trained language model (PLM) for all forms of text, including unstructured text, semi-structured text, and well-structured text.
