Large language models are increasingly expected to estimate their own uncertainty, with confidence signals being used to guide retrieval, adaptive compute allocation, routing, and selective abstention. In this work, we investigate whether confidence signals measured at different temporal stages of problem solving encode distinct forms of epistemic information, and whether such signals remain reliable beyond English-centric settings. Inspired by classical meta-memory theory, we distinguish between anticipatory pre-solve confidence (Feeling-of-Knowing, FOK) and retrospective post-solve confidence (Judgement-of-Learning, JOL). We evaluate these signals across frontier LLMs, multiple reasoning domains, translated Marathi benchmarks, and a newly constructed benchmark of genuinely local Marathi factual questions. Across settings, the cheaper pre-solve signal remains weakly calibrated, while post-solve confidence generally provides stronger epistemic alignment despite additional inference cost. We further find that confidence behaviour transfers surprisingly well under direct language translation, with calibration trends largely preserved between English and Marathi. In contrast, confidence reliability degrades substantially on local Marathi knowledge, suggesting that linguistic transfer alone is insufficient to capture the challenges posed by region-specific information needs. Finally, hallucinated responses maintain persistently elevated confidence even after answer generation, highlighting important limitations of current LLM self-assessment mechanisms.

As LLMs grow more powerful, their most profound achievement may be recognising when to say “I don’t know”. Existing studies on LLM self-knowledge have been largely constrained by human-defined notions of feasibility, often neglecting the reasons behind unanswerability by LLMs and failing to study deficient types of self-knowledge. This study aims to obtain intrinsic insights into different types of LLM self-knowledge with a novel methodology: allowing them the flexibility to set their own feasibility boundaries and then analysing the consistency of these limits. We find that even frontier models like GPT-4o and Mistral Large are not sure of their own capabilities more than 80% of the time, highlighting a significant lack of trustworthiness in responses. Our analysis of confidence balance in LLMs indicates that models swing between overconfidence and conservatism in feasibility boundaries depending on task categories and that the most significant self-knowledge weaknesses lie in temporal awareness and contextual understanding. These difficulties in contextual comprehension additionally lead models to question their operational boundaries, resulting in considerable confusion within the self-knowledge of LLMs. We make our code and results available publicly.

LaTeX’s precision and flexibility in typesetting have made it the gold standard for the preparation of scientific documentation. Large Language Models (LLMs) present a promising opportunity for researchers to produce publication-ready material using LaTeX with natural language instructions, yet current benchmarks completely lack evaluation of this ability. By introducing TeXpert, our benchmark dataset with natural language prompts for generating LaTeX code focused on components of scientific documents across multiple difficulty levels, we conduct an in-depth analysis of LLM performance in this regard and identify frequent error types. Our evaluation across open and closed-source LLMs highlights multiple key findings: LLMs excelling on standard benchmarks perform poorly in LaTeX generation with a significant accuracy drop-off as the complexity of tasks increases; open-source models like DeepSeek v3 and DeepSeek Coder strongly rival closed-source counterparts in LaTeX tasks; and formatting and package errors are unexpectedly prevalent, suggesting a lack of diverse LaTeX examples in the training datasets of most LLMs. Our dataset, code, and model evaluations are available on GitHub at https://github.com/knowledge-verse-ai/TeXpert.