6 accepted papers
Symbolic regression (SR) stands as a cornerstone of scientific discovery, deriving mathematical expressions from observing data. Recent advances incorporate large language models (LLMs) into evolutionary optimization, typically relying on iterative refinement driven by scalar feedback (e.g., mean sq…
Web tasks, which involve processing data from online resources, challenge agents to generalize beyond fixed knowledge to unseen task contexts. Learning from experience, the ability to derive reusable patterns from past tasks, is crucial for improving generalization. However, existing methods focus o…
Classifiers often learn to be biased corresponding to the class-imbalanced dataset under the semi-supervised learning (SSL) set. While previous work tries to appropriately re-balance the classifiers by subtracting a class-irrelevant image's logit, we further utilize a cheaper form of consistency gra…
Physics problems constitute a significant aspect of reasoning, necessitating complicated reasoning ability and abundant physics knowledge. However, existing large language models (LLMs) frequently fail due to a lack of knowledge or incorrect knowledge application. To mitigate these issues, we propos…
Logical reasoning has been an ongoing pursuit in the field of AI. Despite significant advancements made by large language models (LLMs), they still struggle with complex logical reasoning problems. To enhance reasoning performance, one promising direction is scalable oversight, which requires LLMs t…
Large language models (LLMs) exhibit powerful reasoning capacity, as evidenced by prior studies focusing on objective topics that with unique standard answers such as arithmetic and commonsense reasoning. However, the reasoning to definite answers emphasizes more on logical thinking, and falls short…