API-Bank is a new benchmark and training dataset for tool-augmented LLMs that shows fine-tuned models can approach GPT-3.5 tool-use effectiveness.
arXiv preprint arXiv:2306.13304
6 Pith papers cite this work, alongside 39 external citations. Polarity classification is still indexing.
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representative citing papers
GAIA benchmark shows humans at 92% accuracy on simple real-world questions far outperform current AI systems at 15%, proposing this gap as a key milestone for general AI.
PTE is a hardware-aware metric that better predicts actual inference latency in tool-integrated reasoning than token counts and reveals that high-PTE trajectories often have lower correctness.
Introduces NoisyToolBench benchmark and Ask-when-Needed framework to improve LLM tool-use performance when user instructions are unclear or incomplete.
A comprehensive survey of PEFT algorithms for large models, covering their performance, overhead, applications, and real-world system implementations.
The paper surveys key large language models, their training methods, datasets, evaluation benchmarks, and future research directions in the field.
citing papers explorer
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API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs
API-Bank is a new benchmark and training dataset for tool-augmented LLMs that shows fine-tuned models can approach GPT-3.5 tool-use effectiveness.
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GAIA: a benchmark for General AI Assistants
GAIA benchmark shows humans at 92% accuracy on simple real-world questions far outperform current AI systems at 15%, proposing this gap as a key milestone for general AI.
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Beyond Accuracy: Unveiling Inefficiency Patterns in Tool-Integrated Reasoning
PTE is a hardware-aware metric that better predicts actual inference latency in tool-integrated reasoning than token counts and reveals that high-PTE trajectories often have lower correctness.
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Learning to Ask: When LLM Agents Meet Unclear Instruction
Introduces NoisyToolBench benchmark and Ask-when-Needed framework to improve LLM tool-use performance when user instructions are unclear or incomplete.
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Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey
A comprehensive survey of PEFT algorithms for large models, covering their performance, overhead, applications, and real-world system implementations.
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Large Language Models: A Survey
The paper surveys key large language models, their training methods, datasets, evaluation benchmarks, and future research directions in the field.