Diversity in LLM mathematical reasoning is critical for exploration, but common diversity metrics mostly capture surface-level variation rather than differences in how a problem is solved. We address this gap by introducing approach-level diversity: variation in strategies across correct solutions to the same problem....
Sangmook Lee, Minbeom Kim, Jeonghye Kim et al.· 0 citations
Medical large language models are commonly adapted with a fixed low-rank budget, even though medical questions differ substantially in confidence, clinical coverage, and cross-domain difficulty. We study adaptive rank budgeting for parameter-efficient medical question answering: for each question, the adapter decides w...
Shucan Ji, Yining Huang, Hongliang Guo· 0 citations
IndicKLAR, an Indic extension of the KLAR-CLC benchmark covering 18 of the 22 scheduled Indian languages, is introduced and a consistent flip point is observed between the transition from incorrect to correct prediction---between the native and code-mixed settings.
Debajyoti Mazumder, Divyanshu Pathak, P. Kodali et al.· arXiv.org· 0 citations
Multimodal Large Language Models (MLLMs) exacerbate safety risks by introducing vulnerabilities across multiple modalities, such as language and vision. Current MLLM safety evaluation tools, however, suffer from major limitations: 1) English-centric dataset construction, and 2) a focus on generic risks that are not tie...
Yongwoo Kim, Sojung An, Yunjin Park et al.· 0 citations
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Recent developments in reasoning capabilities have enabled large language models to solve increasingly complex mathematical, symbolic, and logical tasks. Interestingly, while reasoning models are often trained to generate monolingual text, these models have also been observed to code-switch (i.e., mix languages). Prior...
Understanding and evaluating multi-step reasoning in LLMs at the level of individual steps remains a key challenge. Process reward models (PRMs) provide a solution by scoring each step, enabling fine-grained supervision and improved reliability. However, training them requires costly human annotation or computationally...
Corentin Royer (International Business Machines), Anna Hedstr\"om (ETH AI Center), Debarun Bhattacharjya (Lirio) et al.· 0 citations
SiDiaC-v.2.0 is the largest comprehensive Sinhala Diachronic Corpus to date, covering a period from 1800 CE to 1955 CE in terms of publication dates, and a historical span from the 5th to the 20th century CE in terms of written dates. The corpus consists of 229k words across 185 literary works that underwent thorough f...
Nevidu Jayatilleke, Nisansa de Silva, Uthpala Nimanthi et al.· 0 citations
This work introduces STAR, a framework for studying how closely the dispositions expressed by LLMs align with those of humans, and finds that LLM recommendations are substantially less diverse than human choices, both within individual models and even across models from different providers.
Amir Taubenfeld, Zorik Gekhman, Lior Nezry et al.· arXiv.org· 7 citations
Text-to-SQL is a key natural language processing task that maps natural language questions to SQL queries, enabling intuitive interaction with web-based databases. Although current methods perform well on benchmarks like BIRD and Spider, they struggle with complex reasoning, domain knowledge, and hypothetical queries,...
Recent advances in text-to-image (T2I) generation have achieved impressive results, yet existing models often struggle with simple or underspecified user prompts due to a distributional gap with their descriptive training captions. This frequently leads to suboptimal image-text alignment, aesthetics, and overall visual...
Ruibo Chen, Jiacheng Pan, Heng Huang et al.· 0 citations
Recent advancements have endowed Large Language Models with impressive general reasoning capabilities. However, these reasoning models often perform worse than non-reasoning models on personalization tasks. While some methods use outcome-based RL to improve personalization reasoning, they fail to supervise the reasonin...
Song Jin, Juntian Zhang, Ruyu Lyu et al.· 0 citations
Large language models (LLMs) often exhibit sycophantic behaviors -- such as excessive agreement with or flattery of the user -- but it is unclear whether these behaviors arise from a single mechanism or multiple distinct processes. We decompose sycophancy into sycophantic agreement and sycophantic praise, contrasting b...
Daniel Vennemeyer, Phan Anh Duong, Tiffany Zhan et al.· 0 citations
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.
MIT News · Artificial Intelligence· news.mit.eduAug 31, 2026
With millions of users across the world, Julia has been used to conduct cutting-edge research and to design new drugs, jet engines, heat pumps, and more.
