Cascaded Batch Prompting introduces a two‑stage method that separates complex reasoning from symbol grounding to address the unpredictability of conventional batch prompting. Experiments on multiple‑choice question answering and natural language inference show that this approach outperforms standard single prompting while maintaining a speedup proportional to batch size. The technique establishes a new state‑of‑the‑art position on the Pareto frontier for efficiency and performance.
By Sho Hoshino, Peinan Zhang
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.
By Inder Preet, Shuxin Lin, Dhaval Patel
arXiv:2410.17021v2 Announce Type: replace
Abstract: Large Language Models with chain-of-thought prompting, such as OpenAI-o1, have shown impressive capabilities in natural language inference tasks. H...
By Xiaochen Wang, Liang Chen, Reza Haf Zhe Yang, Yiru Wang, Xiangdi Meng, Kunhao Pan, Zhifang Sui, Junqing He
arXiv:2607. 28657v1 Announce Type: new Abstract: Large Language Models (LLMs) often require carefully crafted prompts to unlock their full potential, which can be a barrier for non-expert users.
By Oliver Savolainen, Emanuele Bastianelli, Hosein Azarbonyad
The paper compares two common ways of evaluating large language models (LLMs): prompting them to answer questions directly and scoring candidate answers using likelihood-based metrics. The authors introduce a new protocol that ranks declarative statements derived from question–answer pairs, and test it across 95 decoder-only models (0.1B–104B parameters) on 10 multiple-choice QA datasets. They find that while prompted answering accuracy improves sharply with model scale and instruction tuning, statement‑likelihood ranking accuracy stays relatively stable, indicating that the two evaluation methods probe different aspects of model behavior.
By Alessandro Bondielli, Lucia Passaro, Davide Bacciu, Alessandro Lenci
arXiv:2607.14109v2 Announce Type: replace
Abstract: Probing the capabilities of Large Language Models (LLMs) and building robust solutions for Multiple-Choice Question Answering (MCQA) remain central...
By Inder Preet, Shuxin Lin, Dhaval Patel