arXiv:2606. 01182v1 Announce Type: cross Abstract: Large Language Models (LLMs) excel at static reasoning tasks, yet their performance often degrades in interactive scenarios where information must be actively acquired through questioning.
By Daniel Arnould, Rashad Aziz, Zixuan Kang, Tanav Changal, Kevin Zhu, Sunishchal Dev, Gabriel Grand, Shreyas Sunil Kulkarni
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. 22961v1 Announce Type: new Abstract: Verbalized Machine Learning (VML) parameterizes a model as a natural-language prompt that an LLM evaluates as f(x; theta).
By Yan Zhang, Shikan Lian, Shibo Li
arXiv:2511. 19829v3 Announce Type: replace Abstract: Prompt optimization has become a central mechanism for eliciting strong performance from LLMs, and recent work has made substantial progress by proposing diverse prompt evaluation metrics and optimization strategies.
By Ke Chen, Yifeng Wang, Hassan Almosapeeh, Haohan Wang
The paper introduces a prompt-response concept model that links the amount of task-relevant information in a prompt to the uncertainty of responses generated by large language models (LLMs). It identifies four sources of response uncertainty—prompt underspecification, model quality, task variability, and semantic redundancy—and demonstrates that uncertainty decreases as prompt informativeness or model quality increases, analogous to epistemic uncertainty in probabilistic models. Experiments on real-world datasets confirm the theoretical predictions and validate the model.
By Ze Yu Zhang, Arun Verma, Finale Doshi-Velez, Bryan Kian Hsiang Low
arXiv:2605. 27642v2 Announce Type: replace-cross Abstract: Soft prompting, also known as continuous prompting, is a parameter-efficient method for tuning LLMs to specific tasks.
By Pitipat Kongsomjit, Suryansh Goyal, Jacob Whitehill