Tool learning with large language models: A survey

C Qu, S Dai, X Wei, H Cai, S Wang, D Yin, J Xu… - Frontiers of Computer …, 2025 - Springer
Recently, tool learning with large language models (LLMs) has emerged as a promising
paradigm for augmenting the capabilities of LLMs to tackle highly complex problems …

The llama 3 herd of models

A Dubey, A Jauhri, A Pandey, A Kadian… - arxiv preprint arxiv …, 2024 - arxiv.org
Modern artificial intelligence (AI) systems are powered by foundation models. This paper
presents a new set of foundation models, called Llama 3. It is a herd of language models …

SEAL: Suite for Evaluating API-use of LLMs

W Kim, A Jagmohan, A Vempaty - arxiv preprint arxiv:2409.15523, 2024 - arxiv.org
Large language models (LLMs) have limitations in handling tasks that require real-time
access to external APIs. While several benchmarks like ToolBench and APIGen have been …

ToolComp: A Multi-Tool Reasoning & Process Supervision Benchmark

V Nath, P Raja, C Yoon, S Hendryx - arxiv preprint arxiv:2501.01290, 2025 - arxiv.org
Despite recent advances in AI, the development of systems capable of executing complex,
multi-step reasoning tasks involving multiple tools remains a significant challenge. Current …

[PDF][PDF] Recommender Systems in the Era of Large Language Model Agents: A Survey

XI ZHU, YU WANG, H GAO, W XU, C WANG, Z LIU… - researchgate.net
Recommender Systems in the Era of Large Language Model Agents: A Survey Page 1
Recommender Systems in the Era of Large Language Model Agents: A Survey XI ZHU∗ …

InfCycle: Learning to Use Tools via Inference Compute and Cycle Consistency

J Li, W Wang, Y Chen, M Zhang - openreview.net
The scaling of inference-time computation in large language models (LLMs) has emerged
as a promising approach for enhancing reasoning capabilities by trading off inference-time …