Daily Papers

Large language models (LLMs) are increasingly deployed in multilingual applications but often generate plausible yet incorrect or misleading outputs, known as hallucinations. While hallucination detection has been studied extensively in English, under-resourced Indian languages remain largely unexplored. We present BHRAM-IL, a benchmark for hallucination recognition and assessment in multiple Indian languages, covering Hindi, Gujarati, Marathi, Odia, along with English. The benchmark comprises 36,047 curated questions across nine categories spanning factual, numerical, reasoning, and linguistic tasks. We evaluate 14 state-of-the-art multilingual LLMs on a benchmark subset of 10,265 questions, analyzing cross-lingual and factual hallucinations across languages, models, scales, categories, and domains using category-specific metrics normalized to (0,1) range. Aggregation over all categories and models yields a primary score of 0.23 and a language-corrected fuzzy score of 0.385, demonstrating the usefulness of BHRAM-IL for hallucination-focused evaluation. The dataset, and the code for generation and evaluation are available on GitHub (https://github.com/sambhashana/BHRAM-IL/) and HuggingFace (https://huggingface.co/datasets/sambhashana/BHRAM-IL/) to support future research in multilingual hallucination detection and mitigation.

We present the stellar mass-metallicity relation (MZR) and mass-metallicity-star formation relation ("fundamental metallicity relation"; FMR) of 18 massive (log(M/M_odot) = 9.5-11) main-sequence galaxies at z~5 from the ALPINE-CRISTAL-JWST sample. This sample complements recent studies by JWST at up to two orders of magnitude lower stellar masses. The metallicities are derived using strong optical lines, and verified by temperature-based oxygen abundance measurements for five galaxies for which faint auroral lines are detected. We find little evolution at the massive end of the MZR between z~5 and cosmic noon at z~2, suggesting a fast metal enrichment at early times. The FMR at z=5 exhibits a 5x larger scatter (preferentially to lower metallicities) compared the local FMR relation. This scatter can be explained by a bursty star formation and the direct build-up of metals in early galaxies as well as differences in age and outflow efficiencies. Capitalizing on all available samples, we find that the observed MZR and FMR over three orders of stellar mass is generally in good agreement with results from cosmological simulation, although some underestimate the metal enrichment at low stellar masses. This may be due to too efficient metal-rich outflows. We show that the ALPINE-CRISTAL-JWST galaxies likely joined the current FMR at z~10 and will evolve into massive (log(M/M_odot)~11.4) galaxies with super-solar metallicities by z=0.

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://medicaldecathlon.com/. In addition, both data and online evaluation are accessible via www.lits-challenge.com.

Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models that natively support multilinguality, coding, reasoning, and tool usage. Our largest model is a dense Transformer with 405B parameters and a context window of up to 128K tokens. This paper presents an extensive empirical evaluation of Llama 3. We find that Llama 3 delivers comparable quality to leading language models such as GPT-4 on a plethora of tasks. We publicly release Llama 3, including pre-trained and post-trained versions of the 405B parameter language model and our Llama Guard 3 model for input and output safety. The paper also presents the results of experiments in which we integrate image, video, and speech capabilities into Llama 3 via a compositional approach. We observe this approach performs competitively with the state-of-the-art on image, video, and speech recognition tasks. The resulting models are not yet being broadly released as they are still under development.

In this report, we introduce the Gemini 2.X model family: Gemini 2.5 Pro and Gemini 2.5 Flash, as well as our earlier Gemini 2.0 Flash and Flash-Lite models. Gemini 2.5 Pro is our most capable model yet, achieving SoTA performance on frontier coding and reasoning benchmarks. In addition to its incredible coding and reasoning skills, Gemini 2.5 Pro is a thinking model that excels at multimodal understanding and it is now able to process up to 3 hours of video content. Its unique combination of long context, multimodal and reasoning capabilities can be combined to unlock new agentic workflows. Gemini 2.5 Flash provides excellent reasoning abilities at a fraction of the compute and latency requirements and Gemini 2.0 Flash and Flash-Lite provide high performance at low latency and cost. Taken together, the Gemini 2.X model generation spans the full Pareto frontier of model capability vs cost, allowing users to explore the boundaries of what is possible with complex agentic problem solving.

This paper introduces Fietje, a family of small language models (SLMs) specifically designed for the Dutch language. The model is based on Phi 2, an English-centric model of 2.7 billion parameters. Fietje demonstrated competitive results with larger language models upon its release. A core emphasis of this work is transparency and reproducibility: Fietje is fully open-source, with model weights, datasets, training, and evaluation code all publicly accessible. The paper discusses the performance of Fietje and many other models on an extensive evaluation suite of benchmarks on reasoning, sentiment analysis, world knowledge, linguistic acceptability and word sense disambiguation. Evaluation results illustrate the rapid progress in the field of LLMs, where recent small models outperform older, larger models that were fine-tuned for Dutch. This trend signals an exciting future for Dutch language processing, suggesting that even compact LLMs are becoming increasingly capable. Furthermore, ongoing and future efforts to adapt LLMs to Dutch are poised to enhance these models even further, broadening their applicability and accessibility. Fietje is only an intermediate step in improving accessibility to language technology for users of the Dutch language.

We present Welzijn.AI as new digital solution for monitoring (mental) well-being in elderly populations, and illustrate how development of systems like Welzijn.AI can align with guidelines on responsible AI development. Three evaluations with different stakeholders were designed to disclose new perspectives on the strengths, weaknesses, design characteristics, and value requirements of Welzijn.AI. Evaluations concerned expert panels and involved patient federations, general practitioners, researchers, and the elderly themselves. Panels concerned interviews, a co-creation session, and feedback on a proof-of-concept implementation. Interview results were summarized in terms of Welzijn.AI's strengths, weaknesses, opportunities and threats. The co-creation session ranked a variety of value requirements of Welzijn.AI with the Hundred Dollar Method. User evaluation comprised analysing proportions of (dis)agreement on statements targeting Welzijn.AI's design characteristics, and ranking desired social characteristics. Experts in the panel interviews acknowledged Welzijn.AI's potential to combat loneliness and extract patterns from elderly behaviour. The proof-of-concept evaluation complemented the design characteristics most appealing to the elderly to potentially achieve this: empathetic and varying interactions. Stakeholders also link the technology to the implementation context: it could help activate an individual's social network, but support should also be available to empower users. Yet, non-elderly and elderly experts also disclose challenges in properly understanding the application; non-elderly experts also highlight issues concerning privacy. In sum, incorporating all stakeholder perspectives in system development remains challenging. Still, our results benefit researchers, policy makers, and health professionals that aim to improve elderly care with technology.