Jeremy Roghair

Joseph Enguehard, Morgane Van Ermengem, Kate Atkinson et al.

Evaluating large language model (LLM) outputs in the legal domain presents unique challenges due to the complex and nuanced nature of legal analysis. Current evaluation approaches either depend on reference data, which is costly to produce, or use standardized assessment methods, both of which have significant limitations for legal applications. Although LLM-as-a-Judge has emerged as a promising evaluation technique, its reliability and effectiveness in legal contexts depend heavily on evaluation processes unique to the legal industry and how trustworthy the evaluation appears to the human legal expert. This is where existing evaluation methods currently fail and exhibit considerable variability. This paper aims to close the gap: a) we break down lengthy responses into 'Legal Data Points' (LDPs), self-contained units of information, and introduce a novel, reference-free evaluation methodology that reflects how lawyers evaluate legal answers; b) we demonstrate that our method outperforms a variety of baselines on both our proprietary dataset and an open-source dataset (LegalBench); c) we show how our method correlates more closely with human expert evaluations and helps improve inter-annotator agreement; and finally d) we open source our Legal Data Points for a subset of LegalBench used in our experiments, allowing the research community to replicate our results and advance research in this vital area of LLM evaluation on legal question-answering.

Jeremy Roghair, Kyungtae Ko, Amir Ehsan Niaraki Asli et al.

Integration of reinforcement learning with unmanned aerial vehicles (UAVs) to achieve autonomous flight has been an active research area in recent years. An important part focuses on obstacle detection and avoidance for UAVs navigating through an environment. Exploration in an unseen environment can be tackled with Deep Q-Network (DQN). However, value exploration with uniform sampling of actions may lead to redundant states, where often the environments inherently bear sparse rewards. To resolve this, we present two techniques for improving exploration for UAV obstacle avoidance. The first is a convergence-based approach that uses convergence error to iterate through unexplored actions and temporal threshold to balance exploration and exploitation. The second is a guidance-based approach using a Domain Network which uses a Gaussian mixture distribution to compare previously seen states to a predicted next state in order to select the next action. Performance and evaluation of these approaches were implemented in multiple 3-D simulation environments, with variation in complexity. The proposed approach demonstrates a two-fold improvement in average rewards compared to state of the art.

Amir Niaraki, Jeremy Roghair, Ali Jannesari

Visual exploration and smart data collection via autonomous vehicles is an attractive topic in various disciplines. Disturbances like wind significantly influence both the power consumption of the flying robots and the performance of the camera. We propose a reinforcement learning approach which combines the effects of the power consumption and the object detection modules to develop a policy for object detection in large areas with limited battery life. The learning model enables dynamic learning of the negative rewards of each action based on the drag forces that is resulted by the motion of the flying robot with respect to the wind field. The algorithm is implemented in a near-real world simulation environment both for the planar motion and flight in different altitudes. The trained agent often performed a trade-off between detecting the objects with high accuracy and increasing the area coverage within its battery life. The developed exploration policy outperformed the complete coverage algorithm by minimizing the traveled path while finding the target objects. The performance of the algorithms under various wind fields was evaluated in planar and 3D motion. During an exploration task with sparsely distributed goals and within a UAV's battery life, the proposed architecture could detect more than twice the amount of goal objects compared to the coverage path planning algorithm in moderate wind field. In high wind intensities, the energy-aware algorithm could detect 4 times the amount of goal objects when compared to its complete coverage counterpart.