Not All Bias is Bad: Balancing Rational Deviations and Cognitive Biases in Large Language Model Reasoning

This paper investigates the nuanced role of biases in the decision-making processes of large language models (LLMs). While conventional research typically aims to eliminate all biases, our study reveals that not all biases are detrimental. By examining rational deviations, involving heuristic shortcuts that enhance decision-making efficiency, we highlight their potential benefits when properly balanced. We introduce the concepts of heuristic moderation and an abstention option, allowing LLMs to abstain from answering when uncertain, thereby reducing error rates and improving decision accuracy. Using our newly developed BRD (Balance Rational Deviations) dataset, our findings demonstrate that appropriately scaled bias inspection enhances model performance and aligns LLM decision-making more closely with human reasoning. This balance improves the reliability and trustworthiness of LLMs and suggests new strategies for future enhancements. Our work offers a fresh perspective on leveraging biases constructively to enhance the practical applications of LLMs, from conversational agents to decision support systems and beyond.

FENet: Focusing Enhanced Network for Lane Detection

Inspired by human driving focus, this research pioneers networks augmented with Focusing Sampling, Partial Field of View Evaluation, Enhanced FPN architecture and Directional IoU Loss - targeted innovations addressing obstacles to precise lane detection for autonomous driving. Experiments demonstrate our Focusing Sampling strategy, emphasizing vital distant details unlike uniform approaches, significantly boosts both benchmark and practical curved/distant lane recognition accuracy essential for safety. While FENetV1 achieves state-of-the-art conventional metric performance via enhancements isolating perspective-aware contexts mimicking driver vision, FENetV2 proves most reliable on the proposed Partial Field analysis. Hence we specifically recommend V2 for practical lane navigation despite fractional degradation on standard entire-image measures. Future directions include collecting on-road data and integrating complementary dual frameworks to further breakthroughs guided by human perception principles. The Code is available at https://github.com/HanyangZhong/FENet.

LLM-SAP: Large Language Model Situational Awareness Based Planning

This work pioneers evaluating emergent planning capabilities based on situational awareness in large language models. We contribute (i) novel benchmarks and metrics for standardized assessment; (ii) a unique dataset to spur progress; and (iii) demonstrations that prompting and multi-agent schemes significantly enhance planning performance in context-sensitive planning tasks. Positioning this within a situated agent and automated planning research, we highlight inherent reliability challenges--efficiently mapping world states to actions without environmental guidance remains open despite simulated domain advances. Although out-of-scope, limitations around validation methodology and data availability indicate exciting directions, including fine-tuning on expanded planning corpora and optimizations for triggering fast latent planning. By conclusively demonstrating current methods' promise and limitations via rigorous comparison, we catalyze investigating reliable goal-directed reasoning for situated agents.