arXiv AI
ProCompNav: Proactive Instance Navigation with Comparative Judgment for Ambiguous User Queries
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arXiv AI
Aug 25
Clarify User Expertise: Towards Proactive Conversational Agents Tailoring Responses to User Proficiency
arXiv:2608.22266v1 Announce Type: new Abstract: In the context of information seeking, conversational agents are undergoing an evolution from reactive tools to proactive, personalized assistants. A c...
arXiv AI
Jul 31
Fewer Clarifications, Better Code: Benchmarking Cross-Session Personalized Ambiguity Adaptation in Coding Assistants
arXiv:2607. 26611v1 Announce Type: new Abstract: AI-assisted coding increasingly translates informal user intent into executable software, yet coding requests often contain ambiguities that recur in user-specific ways across tasks and sessions.
arXiv AI
Sep 1
MUDDLE: Measuring Understanding of Documents under Distractor and Length Effects
MUDDLE is a benchmark designed to disentangle the effects of document length and topical distractors on document question‑answering systems. It contains 270 human‑annotated questions, each tested in five conditions: the source alone, the source with two or four hard negatives (topically similar), and the source with two or four random distractors matched in length and provenance. Experiments with GPT‑5‑mini show that hard negatives reduce accuracy more than length‑matched random distractors, indicating that topical similarity is a more significant source of error than length alone.
arXiv AI
Jul 17
Automatically Evolving Prompt Guidelines for Task-Specific Optimization
arXiv:2607. 14105v1 Announce Type: cross Abstract: For Large Language Models to reliably answer user queries, users must clearly specify requirements, context, and constraints.
arXiv AI
Jun 26
Ask, Don't Judge: Binary Questions for Interpretable LLM Evaluation and Self-Improvement
arXiv:2606. 27226v1 Announce Type: new Abstract: Evaluating LLM outputs remains a major bottleneck in NLP: human evaluation is expensive and slow, lexical metrics correlate poorly with human judgments on open-ended generation, and holistic LLM judges often produce opaque scores that are hard to debug.
arXiv Machine Learning
Aug 28
$p1$: Better Prompt Optimization with Fewer Prompts
The paper investigates why prompt optimization works better for some tasks than others by decomposing reward variance into response variance and system‑prompt variance. It finds that optimization succeeds when system‑prompt variance dominates, and that adding more user prompts can actually reduce this variance, especially on heterogeneous datasets. To address this, the authors propose $p1$, a filtering method that selects a small set of high‑variance user prompts, which improves optimization on reasoning benchmarks and even allows a system prompt trained on just two AIME 24 prompts to generalize well.