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.
arXiv:2607. 03426v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit strong reasoning and world-knowledge capabilities, yet often struggle to gather information effectively across the multi-turn interactions required in sequential decision-making settings.
arXiv:2407. 03884v4 Announce Type: replace-cross Abstract: Dialogue agents powered by Large Language Models (LLMs) show superior performance in various tasks.
arXiv:2605.25831v2 Announce Type: replace-cross Abstract: Large language models (LLMs) define a distribution over text, which can be viewed as a probabilistic representation of uncertainty: sampling...
BayesPrompt proposes a Bayesian approach to prompt optimisation for large language models, aiming to generate prompts that are both efficient in perplexity and human readable. The authors argue that traditional optimisation methods produce unintelligible pseudoprompts due to the ill‑posed nature of the task. Their algorithm samples prompts from a posterior distribution, and experiments on real data show marked improvements over state‑of‑the‑art alternatives across several metrics.
arXiv:2511.10661v2 Announce Type: replace Abstract: It is increasingly important to evaluate the characteristics of systems based on large language models (LLMs). Evaluations in this context often re...
arXiv:2604. 03924v2 Announce Type: replace-cross Abstract: Goal-oriented conversational systems require making sequential decisions under uncertainty about the user's intent, where the algorithm must balance information acquisition and target commitment over multiple turns.
arXiv:2608.30426v1 Announce Type: new Abstract: Current dialogue systems struggle with dynamic information retrieval, often leading to hallucinations and lower response accuracy. We address this by a...
arXiv:2601. 07994v5 Announce Type: replace-cross Abstract: Large Language Models (LLMs) increasingly operate over long-form dialogues with frequent topic shifts.
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:2607. 03093v1 Announce Type: cross Abstract: Thinking has emerged as a critical capability for Large Language Models (LLMs) tackling complex tasks.
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.
StateTree is a reinforcement learning approach that improves long‑term dialogue reasoning by building a tree‑structured auxiliary task from limited dialogue data. The method embeds key‑value records across multiple sessions into a binary tree, requiring the model to traverse from root to leaf, retrieve records, compare timestamps, and identify a target question among distractors. Curriculum RL training increases tree depth, and a compositional variant trains the model to combine partial reasoning fragments, enabling cross‑session retrieval, temporal reasoning, knowledge updates, and multi‑hop reasoning while generalizing from 10K‑token to 128K‑token contexts.