Training expert LLMs in domains with scarce fine-grained annotated data is admittedly challenging, often relying on multiple-choice questions (MCQs).However, standard outcomebased reinforcement learning (RL) on MCQs is risky.While outcome-based RL may improve accuracy, it frequently compromises the reasoning process, yielding internally inconsistent rationales that diverge from the final predictions.Existing solutions to supervise the reasoning process, such as large-scale Process Reward Models (PRMs), are prohibitively expensive.To address this, we propose CLARITY, a costeffective RL framework that enhances reasoning quality using a small, general-purpose LLM only.CLARITY integrates a consistency-aware reward mechanism with a 2-stage refine-thenmonitor training pipeline to enhance reasoning consistency, and a dynamic data reformulation strategy to better exploit annotated data available.Experiments demonstrate that CLARITY can improve the consistency of responses by 16.5% over standard outcome-based RL, and bring an improvement of 7.5% in final accuracy.Human evaluations further confirm substantial gains in factual correctness and reasoning coherence, leading to more trustworthy model outputs.Thus, CLARITY offers a generalizable solution that enables smaller models to effectively guide expert LLM training by monitoring reasoning consistency. 1

Report generation (RG) faces challenges in understanding complex medical images and establishing cross-modal semantic alignment in radiology image-report pairs. Previous methods often overlook fine-grained cross-modal interaction, leading to insufficient understanding of detailed information. Recently, various large multimodal models have been proposed for image-text tasks. However, such models still underperform on rare domain tasks like understanding complex medical images. To address these limitations, we develop a new framework of Knowledge-guided Implicit vision-language Alignment for radiology report generation, named KIA. To better understand medical reports and images and build alignment between them, multi-task implicit alignment is creatively introduced, forming comprehensive understanding of medical images and reports. Additionally, to further meet medical refinement requirements, we design novel masking strategies guided by medical knowledge to enhance pathological observation and anatomical landm

While existing query-based 3D end-to-end visual trackers integrate detection and tracking via the *tracking-by-attention* paradigm, these two chicken-and-egg tasks encounter optimization difficulties when sharing the same parameters. Our findings reveal that these difficulties arise due to two inherent constraints on the self-attention mechanism, i.e., over-deduplication for object queries and self-centric attention for track queries. In contrast, removing self-attention mechanism not only minimally impacts regression predictions of the tracker, but also tends to generate more latent candidate boxes. Based on these analyses, we present SynCL, a novel plug-and-play synergistic training strategy designed to co-facilitate multi-task learning for detection and tracking. Specifically, we propose a Task-specific Hybrid Matching module for a weight-shared cross-attention-based decoder that matches the targets of track queries with multiple object queries to exploit promising candidates overlooked by the self-attention mechanism and the bipartite matching. To flexibly select optimal candidates for the one-to-many matching, we also design a Dynamic Query Filtering module controlled by model training status. Moreover, we introduce Instance-aware Contrastive Learning to break through the barrier of self-centric attention for track queries, effectively bridging the gap between detection and tracking. Without additional inference costs, SynCL consistently delivers improvements in various benchmarks and achieves state-of-the-art performance with $58.9\%$ AMOTA on the nuScenes dataset. Code and raw results are available at <https://github.com/shubolin028/SynCL>.

FEVEROUS is a fact extraction and verification task that requires systems to extract evidence of both sentences and table cells from a Wikipedia dump, then predict the veracity of the given claim accordingly.Existing works extract evidence in the two formats separately, ignoring potential connections between them.In this paper, we propose a Unified Evidence Extraction model (UNIFEE), which uses a mixed evidence graph to extract the evidence in both formats.With the carefully-designed unified evidence graph, UNIFEE allows evidence interactions among all candidates in both formats at similar granularity.Experiments show that, with information aggregated from related evidence candidates in the fusion graph, UNIFEE can make better decisions about which evidence should be kept, especially for claims requiring multi-hop reasoning or a combination of tables and texts.Thus it outperforms all previous evidence extraction methods and brings significant improvement in the subsequent claim verification step.

Open-domain fact verification is the task of verifying claims in natural language texts against extracted evidence.FEVEROUS is a benchmark that requires extracting and integrating both unstructured and structured evidence to verify a given claim.Previous models suffer from low recall of structured evidence extraction, i.e., table extraction and cell selection.In this paper, we propose a simple but effective method to enhance the extraction of structured evidence by leveraging the row and column semantics of tables.Our method comprises two components: (i) a coarse-grained table extraction module that selects tables based on rows and columns relevant to the claim and (ii) a fine-grained cell selection graph that combines both formats of evidence and enables multihop and numerical reasoning.We evaluate our method on FEVEROUS and achieve an evidence recall of 60.01% on the test set, which is 6.14% higher than the previous state-of-theart performance.Our results demonstrate that our method can extract tables and select cells effectively, and provide better evidence sets for verdict prediction.Our code is released at https://github.com/

Neural Radiance Fields (NeRFs) have made great success in representing complex 3D scenes with high-resolution details and efficient memory. Nevertheless, current NeRF - based pose estimators have no initial pose prediction and are prone to local optima during optimization. In this paper, we present LATITUDE: Global Localization with Truncated Dynamic Low-pass Filter, which introduces a two-stage localization mechanism in city-scale NeRF. In place recognition stage, we train a regressor through images generated from trained NeRFs, which provides an initial value for global localization. In pose optimization stage, we minimize the residual between the observed image and rendered image by directly optimizing the pose on the tangent plane. To avoid falling into local optimum, we introduce a Truncated Dynamic Low-pass Filter (TDLF) for coarse-to-fine pose registration. We evaluate our method on both synthetic and real-world data and show its potential applications for high-precision navigation in large-scale city scenes. Codes and dataset will be publicly available at https://github.com/jike5/LATITUDE.

Nan Hu, Zirui Wu, Yuxuan Lai, Xiao Liu, Yansong Feng. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.