3 papers
cs.CL2024
Why does in-context learning fail sometimes? Evaluating in-context learning on open and closed questions
Xiang Li, Haoran Tang, Siyu Chen +3
We measure the performance of in-context learning as a function of task novelty and difficulty for open and closed questions. For that purpose, we created a novel benchmark consist…
cs.LG2024
Toward Structure Fairness in Dynamic Graph Embedding: A Trend-aware Dual Debiasing Approach
Yicong Li, Yu Yang, Jiannong Cao +3
Recent studies successfully learned static graph embeddings that are structurally fair by preventing the effectiveness disparity of high- and low-degree vertex groups in downstream…
cs.CV2024
ST-LLM: Large Language Models Are Effective Temporal Learners
Ruyang Liu, Chen Li, Haoran Tang +3
Large Language Models (LLMs) have showcased impressive capabilities in text comprehension and generation, prompting research efforts towards video LLMs to facilitate human-AI inter…