Large language models (LLMs) have demonstrated remarkable capabilities in various domains, including radiology report generation.Previous approaches have attempted to utilize multimodal LLMs for this task, enhancing their performance through the integration of domainspecific knowledge retrieval.However, these approaches often overlook the knowledge already embedded within the LLMs, leading to redundant information integration.To address this limitation, we propose RADAR, a framework for enhancing radiology report generation with supplementary knowledge injection.RADAR improves report generation by systematically leveraging both the internal knowledge of an LLM and externally retrieved information.Specifically, it first extracts the model's acquired knowledge that aligns with expert imagebased classification outputs.It then retrieves relevant supplementary knowledge to further enrich this information.Finally, by aggregating both sources, RADAR generates more accurate and informative radiology reports.Extensive experiments on MIMIC-CXR, CHEXPERT-PLUS, and IU X-RAY demonstrate that our model outperforms state-of-the-art LLMs in both language quality and clinical accuracy 1 .

Kaishuai Xu, Tiezheng Yu, Wenjun Hou, Yi Cheng, Chak Tou Leong, Liangyou Li, Xin Jiang, Lifeng Shang, Qun Liu, Wenjie Li. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Self-consistency-based approaches, which involve repeatedly sampling multiple outputs and selecting the most consistent one as the final response, prove to be remarkably effective in improving the factual accuracy of large language models. Nonetheless, existing methods usually have strict constraints on the task format, largely limiting their applicability. In this paper, we present Integrative Decoding (ID), to unlock the potential of self-consistency in open-ended generation tasks. ID operates by constructing a set of inputs, each prepended with a previously sampled response, and then processes them concurrently, with the next token being selected by aggregating of all their corresponding predictions at each decoding step. In essence, this simple approach implicitly incorporates self-consistency in the decoding objective. Extensive evaluation shows that ID consistently enhances factuality over a wide range of language models, with substantial improvements on the TruthfulQA (+11.2%), Biographies (+15.4%) and LongFact (+8.5%) benchmarks. The performance gains amplify progressively as the number of sampled responses increases, indicating the potential of ID to scale up with repeated sampling.

Large language models (LLMs) have demonstrated remarkable reasoning capabilities through test-time scaling approaches, particularly when fine-tuned with chain-of-thought (CoT) data distilled from more powerful large reasoning models (LRMs). However, these reasoning chains often contain verbose elements that mirror human problem-solving, categorized as progressive reasoning (the essential solution development path) and functional elements (verification processes, alternative solution approaches, and error corrections). While progressive reasoning is crucial, the functional elements significantly increase computational demands during test-time inference. We introduce PIR (Perplexity-based Importance Refinement), a principled framework that quantitatively evaluates the importance of each reasoning step based on its impact on answer prediction confidence. PIR systematically identifies and selectively prunes only low-importance functional steps while preserving all progressive reasoning components, creating optimized training data that maintains the integrity of the core solution path while reducing verbosity. Models fine-tuned on PIR-optimized data exhibit superior test-time scaling properties, generating more concise reasoning chains while achieving improved accuracy (+0.9\% to +6.6\%) with significantly reduced token usage (-3\% to -41\%) across challenging reasoning benchmarks (AIME, AMC, and GPQA Diamond). Our approach demonstrates strong generalizability across different model sizes, data sources, and token budgets, offering a practical solution for deploying reasoning-capable LLMs in scenarios where efficient test-time scaling, response time, and computational efficiency are valuable constraints. Code and dataset are available at the [LIMOPro GitHub repository.](https://github.com/GAIR-NLP/LIMOPro)

Depression is a critical concern in global mental health, prompting extensive research into AIbased detection methods.Among various AI technologies, Large Language Models (LLMs) stand out for their versatility in mental healthcare applications.However, their primary limitation arises from their exclusive dependence on textual input, which constrains their overall capabilities.Furthermore, the utilization of LLMs in identifying and analyzing depressive states is still relatively untapped.In this paper, we present an innovative approach to integrating acoustic speech information into the LLMs framework for multimodal depression detection.We investigate an efficient method for depression detection by integrating speech signals into LLMs utilizing Acoustic Landmarks.By incorporating acoustic landmarks, which are specific to the pronunciation of spoken words, our method adds critical dimensions to text transcripts.This integration also provides insights into the unique speech patterns of individuals, revealing the potential mental states of individuals.Evaluations of the proposed approach on the DAIC-WOZ dataset reveal state-of-the-art results when compared with existing Audio-Text baselines.In addition, this approach is not only valuable for the detection of depression but also represents a new perspective in enhancing the ability of LLMs to comprehend and process speech signals.

This paper explores the task of radiology report generation, which aims at generating free-text descriptions for a set of radiographs.One significant challenge of this task is how to correctly maintain the consistency between the images and the lengthy report.Previous research explored solving this issue through planningbased methods, which generate reports only based on high-level plans.However, these plans usually only contain the major observations from the radiographs (e.g., lung opacity), lacking much necessary information, such as the observation characteristics and preliminary clinical diagnoses.To address this problem, the system should also take the image information into account together with the textual plan and perform stronger reasoning during the generation process.In this paper, we propose an Observation-guided radiology Report GenerAtioN framework (ORGAN).It first produces an observation plan and then feeds both the plan and radiographs for report generation, where an observation graph and a tree reasoning mechanism are adopted to precisely enrich the plan information by capturing the multiformats of each observation.Experimental results demonstrate that our framework outperforms previous state-of-the-art methods regarding text quality and clinical efficacy.1