Tool-integrated reasoning (TIR) enables large language models (LLMs) to invoke external tools for tasks beyond their internal capacity but often suffers from tool overuse.Existing approaches leverage imitation learning or reward shaping to improve efficiency, yet mainly target single-tool scenarios and ignore the varying invocation costs across tools in multi-tool reasoning (MTIR). To address these gaps, we propose EMTIR-GRPO, a simple yet effective RL algorithm for cost-aware MTIR. Built upon GRPO, we introduce a composite reward considering format completeness, answer correctness, and tool efficiency.By incorporating a cost-aware coefficient with group optimal cost estimation, EMTIR-GRPO explicitly models heterogeneous tool costs and encourages more cost-effective tool-use strategies. Experiments on MTIR-QA and MTIR-TC demonstrate significant efficiency gains (e.g., 𝛥+10.9 on Tool-Star-7B and 𝛥+3.6 on ReCall-7B) while maintaining or even improving accuracy (e.g., 55.4 vs. 52.0 on Tool-Star-7B). Additional budget-constrained and tool-free evaluations further validate its effectiveness in maximizing cost-efficiency and reducing cognitive offloading.

The performance of large language models (LLMs) is closely tied to their training data, which can include copyrighted material or private information, raising legal and ethical concerns. Additionally, LLMs face criticism for dataset contamination and internalizing biases. To address these issues, the Pre-Training Data Detection (PDD) task was proposed to identify if specific data was included in an LLM’s pre-training corpus. However, existing PDD methods often rely on superficial features like prediction confidence and loss, resulting in mediocre performance. To improve this, we introduce NA-PDD, a novel algorithm analyzing differential neuron activation patterns between training and non-training data in LLMs. This is based on the observation that these data types activate different neurons during LLM inference. We also introduce CCNewsPDD, a temporally unbiased benchmark employing rigorous data transformations to ensure consistent time distributions between training and non-training data. Our experiments demonstrate that NA-PDD significantly outperforms existing methods across three benchmarks and multiple LLMs.

Chain-of-thought (CoT) and subsequent methods adopted a deductive paradigm that decomposes the reasoning process, demonstrating remarkable performances across NLP tasks. However, such a paradigm faces the challenge of getting bogged down in low-level semantic details, hindering large language models (LLMs) from correctly understanding, selecting, and compositing conditions. In this work, we present Overarching Prompting (OaP), a simple prompting method that elicits the high-level thinking of LLMs. Specifically, OaP first abstracts the whole problem into a simplified archetype and formulates strategies grounded in concepts and principles, establishing an overarching perspective for guiding reasoning. We conducted experiments with SoTA models, including ChatGPT, InstructGPT, and Llama3-70B-instruct, and received promising performances across tasks including Knowledge QA, Mathematical, and Open-Domain Reasoning. For instance, OaP improved ChatGPT and CoT by 19.0% and 3.1% on MMLU’s College Physics, 8.8% and 2.3% on GSM8k, and 10.3% and 2.5% on StrategyQA, respectively.