arXiv:2606. 07548v1 Announce Type: cross Abstract: The MedHopQA challenge presents a critical test for Large Language Models (LLMs): complex, multi-hop reasoning in the high-stakes biomedical domain.
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arXiv:2608. 07494v1 Announce Type: cross Abstract: AI tools like ChatGPT and DeepSeek, powered by Large Language Models (LLMs), allow users to obtain instant and effective content responses simply by typing requests, such as ``plan a three-day Vienna trip'', ``solve the attached mathematical problem'', ``draft an email to inquire review progress'', etc.
The paper evaluates how well current Large Language Models can translate natural language goals, written by video game testers, into well‑formed PDDL targets for classical planning. Using a carefully designed prompt template, six state‑of‑the‑art LLMs were tested on correctness, speed, and error tendencies with real‑world benchmarks. All models achieved high correctness (>92%), with Gemini 2.5 Flash reaching 96% accuracy and the fewest false positives, while GPT‑4.1 was the fastest, yet differences in performance and occasional failures due to ambiguity and domain limits remain.
arXiv:2606. 11459v1 Announce Type: cross Abstract: Large Language Models are highly sensitive to prompt formulation, necessitating automatic prompt optimization to unlock their full potential.
arXiv:2601. 02896v3 Announce Type: replace Abstract: Controlling emergent behavioral personas (e.
arXiv:2607. 14109v1 Announce Type: cross Abstract: Probing the capabilities of Large Language Models (LLMs) and building robust solutions for Multiple-Choice Question Answering (MCQA) remain central challenges in natural language understanding.
arXiv:2608. 01366v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are integral to complex intellectual tasks, yet output quality remains constrained by user-provided prompts.