Abstract Reasoning with Distracting Features
Abstraction reasoning is a long-standing challenge in artificial intelligence. Recent studies suggest that many of the deep architectures that have triumphed over other domains failed to work well in abstract reasoning. In this paper, we first illustrate that one of the main challenges in such a reasoning task is the presence of distracting features, which requires the learning algorithm to leverage counterevidence and to reject any of the false hypotheses in order to learn the true patterns. We later show that carefully designed learning trajectory over different categories of training data can effectively boost learning performance by mitigating the impacts of distracting features. Inspired by this fact, we propose feature robust abstract reasoning (FRAR) model, which consists of a reinforcement learning based teacher network to determine the sequence of training and a student network for predictions. Experimental results demonstrated strong improvements over baseline algorithms and we are able to beat the state-of-the-art models by 18.7% in the RAVEN dataset and 13.3% in the PGM dataset.
Code (1)
Tasks
Reinforcement LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
AbstRaL: Augmenting LLMs' Reasoning by Reinforcing Abstract Thinking
Recent studies have shown that large language models (LLMs), especially smaller ones, often lack robustness in their reasoning. I.e., they tend to experience performance drops when faced with distribution shifts, such as…
Reinforcement Learning (RL)Iterative LLM-Based Generation and Refinement of Distracting Conditions in Math Word Problems
Mathematical reasoning serves as a crucial testbed for the intelligence of large language models (LLMs), and math word problems (MWPs) are a popular type of math problems. Most MWP datasets consist of problems containing…
Mathematical ReasoningCops-Ref: A new Dataset and Task on Compositional Referring Expression Comprehension
Referring expression comprehension (REF) aims at identifying a particular object in a scene by a natural language expression. It requires joint reasoning over the textual and visual domains to solve the problem. Some pop…
Referring ExpressionReferring Expression ComprehensionVisual ReasoningA Two-Stream AMR-enhanced Model for Document-level Event Argument Extraction
Most previous studies aim at extracting events from a single sentence, while document-level event extraction still remains under-explored. In this paper, we focus on extracting event arguments from an entire document, wh…
Abstract Meaning RepresentationDocument-level Event ExtractionEvent Argument ExtractionEvent Extraction+1The Distracting Effect: Understanding Irrelevant Passages in RAG
A well-known issue with Retrieval Augmented Generation (RAG) is that retrieved passages that are irrelevant to the query sometimes distract the answer-generating LLM, causing it to provide an incorrect response. In this …
Binary ClassificationRAGRetrieval-augmented Generation