Retrieval-Augmented Language Models boost task performance, owing to the retriever that provides external knowledge.Although crucial, the retriever primarily focuses on semantics relevance, which may not always be effective for generation.Thus, utility-based retrieval has emerged as a promising topic, prioritizing passages that provide valid benefits for downstream tasks.However, due to insufficient understanding, capturing passage utility accurately remains unexplored.This work proposes SCARLet, a framework for training utility-based retrievers in RALMs, which incorporates two key factors, multi-task generalization and inter-passage interaction.First, SCAR-Let constructs shared context on which training data for various tasks is synthesized.This mitigates semantic bias from context differences, allowing retrievers to focus on learning task-specific utility and generalize across tasks.Next, SCARLet uses a perturbation-based attribution method to estimate passage-level utility for shared context, which reflects interactions between passages and provides more accurate feedback.We evaluate our approach on ten datasets across various tasks, both indomain and out-of-domain, showing that retrievers trained by SCARLet consistently improve the overall performance of RALMs.

Yilong Xu, Jinhua Gao, Xiaoming Yu, Baolong Bi, Huawei Shen, Xueqi Cheng. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

In nature, the behavior of many complex systems can be described by parsimonious math equations. Symbolic Regression (SR) is defined as the task of automatically distilling equations from limited data. Keen efforts have been placed on tackling this issue and demonstrated success in SR. However, there still exist bottlenecks that current methods struggle to break, when the expressions we need to explore tend toward infinity and especially when the underlying math formula is intricate. To this end, we propose a novel Reinforcement Symbolic Regression Machine (RSRM) that masters the capability of uncovering complex math equations from only scarce data. The RSRM model is composed of three key modules: (1) a Monte Carlo tree search (MCTS) agent, designed for exploration, that explores optimal math expression trees consisting of pre-defined math operators and variables, (2) a Double Q-learning block, designed for exploitation, that helps reduce the feasible search space of MCTS via properly understanding the distribution of reward, and (3) a modulated sub-tree discovery block that heuristically learns and defines new math operators to improve representation ability of math expression trees. Binding of these modules yields the SOTA performance of RSRM in SR as demonstrated by multiple benchmark datasets. The RSRM shows clear superiority over several representative baseline models.

Although the Segment Anything Model (SAM) has achieved impressive results in many segmentation tasks and benchmarks, its performance noticeably deteriorates when applied to high-resolution images for high-precision segmentation, limiting it's usage in many real-world applications.In this work, we explored transferring SAM into the domain of high-resolution images and proposed Pi-SAM. Compared to the original SAM and its variants, Pi-SAM demonstrates the following superiorities: Firstly, Pi-SAM possesses a strong perception capability for the extremely fine details in high-resolution images, enabling it to generate high-precision segmentation masks. As a result,Pi-SAM significantly surpasses previous methods in four high-resolution datasets. Secondly, Pi-SAM supports more precise user interactions. In addition to the native promptable ability of SAM, Pi-SAM allows users to interactively refine the segmentation predictions simply by clicking. While the original SAM fails to achieve this on high-resolution images. Thirdly, building upon SAM, Pi-SAM introduces very few additional parameters and computational costs and ensures highly efficient model fine-tuning to achieve the above performance.