PEEM is a multi-criteria LLM-based evaluator for prompts and responses that aligns with standard accuracy while enabling zero-shot prompt optimization via feedback.
arXiv preprint arXiv:2401.08189 , year=
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MemoPilot trains memory updates for LLM agents via multi-turn GRPO on RPS and poker, achieving top Elo scores and outperforming baselines including DeepSeek-V3.2.
LLM use for essay writing correlates with reduced brain network connectivity, lower self-reported ownership, and poorer recall of one's own content compared to unaided or search-based writing.
AIR excels on label-remapping classification tasks while KNN retrieval leads on closed-book QA and fine-tuning leads on structured extraction and event-order reasoning, showing task-dependent adaptation performance.
S^3-R1 generates synthetic multi-hop questions and uses combined intermediate and final rewards to train RL models for retrieval and answering, reporting up to 10% better out-of-domain generalization.
citing papers explorer
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PEEM: Prompt Engineering Evaluation Metrics for Interpretable Joint Evaluation of Prompts and Responses
PEEM is a multi-criteria LLM-based evaluator for prompts and responses that aligns with standard accuracy while enabling zero-shot prompt optimization via feedback.
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From Player to Master: Enhancing Test-Time Learning of LLM Agents via Reinforcement Learning over Memory
MemoPilot trains memory updates for LLM agents via multi-turn GRPO on RPS and poker, achieving top Elo scores and outperforming baselines including DeepSeek-V3.2.
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Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task
LLM use for essay writing correlates with reduced brain network connectivity, lower self-reported ownership, and poorer recall of one's own content compared to unaided or search-based writing.
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Automated Instruction Revision (AIR): A Structured Comparison of Task Adaptation Strategies for LLM
AIR excels on label-remapping classification tasks while KNN retrieval leads on closed-book QA and fine-tuning leads on structured extraction and event-order reasoning, showing task-dependent adaptation performance.
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$S^3$-R1: Learning to Retrieve and Answer Step-by-Step with Synthetic Data
S^3-R1 generates synthetic multi-hop questions and uses combined intermediate and final rewards to train RL models for retrieval and answering, reporting up to 10% better out-of-domain generalization.