Keqin Li
Biographic Data
| ID | 10054376 |
|---|---|
| NAME | Keqin Li |
| GIVEN NAMES | Keqin |
| FAMILY NAME | Li |
| SIGNATURE | LI K |
| AFFILIATIONS | SUNY New Paltz |
| ORCID | 0000-0001-5224-4048 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2025 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Explaining Sentiments
Sentiment analysis of social media platforms is crucial for extracting actionable insights from unstructured textual data. However, modern sentiment analysis models using deep learning lack explainability, acting as black box and limiting trust. This study focuses on improving the explainability of sentiment analysis models of social media platforms by leveraging explainable artificial intelligence (XAI). We propose a novel explainable sentiment …
Ccde
Automatic evaluation metrics not only play a vital role in developing dialogue and interactive systems but also have a great impact on social activities in our daily life. However, previous specialized metrics for evaluating dialogues exhibit a relatively low correlation with human judgments. In addition, today’s state-of-the-art (SOTA) evaluators that leverage large language models (LLMs) are challenging to deploy in real-world applications due …
Efficient Inference Scheduling With Edge and Cloud Collaboration for LLMs Under Resource Constraints
Large language models (LLMs) have transformed artificial intelligence (AI) applications but require substantial computational resources, posing challenges for real-time deployment. Cloud computing offers powerful resources but suffers from high latency and bandwidth costs. In contrast, edge computing reduces latency but is limited by its computational capacity for large-scale models. Consequently, edge–cloud collaboration emerges as a critical ap…
No prominent works on this page.
Explaining Sentiments
Sentiment analysis of social media platforms is crucial for extracting actionable insights from unstructured textual data. However, modern sentiment analysis models using deep learning lack explainability, acting as black box and limiting trust. This study focuses on improving the explainability of sentiment analysis models of social media platforms by leveraging explainable artificial intelligence (XAI). We propose a novel explainable sentiment …
Ccde
Automatic evaluation metrics not only play a vital role in developing dialogue and interactive systems but also have a great impact on social activities in our daily life. However, previous specialized metrics for evaluating dialogues exhibit a relatively low correlation with human judgments. In addition, today’s state-of-the-art (SOTA) evaluators that leverage large language models (LLMs) are challenging to deploy in real-world applications due …
Efficient Inference Scheduling With Edge and Cloud Collaboration for LLMs Under Resource Constraints
Large language models (LLMs) have transformed artificial intelligence (AI) applications but require substantial computational resources, posing challenges for real-time deployment. Cloud computing offers powerful resources but suffers from high latency and bandwidth costs. In contrast, edge computing reduces latency but is limited by its computational capacity for large-scale models. Consequently, edge–cloud collaboration emerges as a critical ap…
Artificial Intelligence (2 works) · Computer Science (2 works) · Topic Modeling (2 works) · Chemistry (1 work) · Cloud computing (1 work) · Cloud Computing and Resource Management (1 work) · Cognitive psychology (1 work) · Counterfactual conditional (1 work) · Counterfactual thinking (1 work) · Distillation (1 work)