API-BLEND: A Comprehensive Corpora for Training and Benchmarking API
  LLMs
There is a growing need for Large Language Models (LLMs) to effectively use
tools and external Application Programming Interfaces (APIs) to plan and
complete tasks. As such, there is tremendous interest in methods that can
acquire sufficient quantities of train and test data that involve calls to
tools / APIs. Two lines of research have emerged as the predominant strategies
for addressing this challenge. The first has focused on synthetic data
generation techniques, while the second has involved curating task-adjacent
datasets which can be transformed into API / Tool-based tasks. In this paper,
we focus on the task of identifying, curating, and transforming existing
datasets and, in turn, introduce API-BLEND, a large corpora for training and
systematic testing of tool-augmented LLMs. The datasets mimic real-world
scenarios involving API-tasks such as API / tool detection, slot filling, and
sequencing of the detected APIs. We demonstrate the utility of the API-BLEND
dataset for both training and benchmarking purposes.
65dc073f0af7e21ba4f8c5eb
Kinjal Basu
claimed_verified
2024-02-26T21:09:40.779Z
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Kinjal Basu
user
kinjalbasu
65dc073f0af7e21ba4f8c5ec
Ibrahim Abdelaziz
claimed_verified
2024-05-07T08:33:58.229Z
6632b81cab69b961af2fcfb4
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Ibrahim A
user
ibrahimabdelaziz
65dc073f0af7e21ba4f8c5ed
Subhajit Chaudhury
claimed_verified
2024-02-26T21:09:38.105Z
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Subhajit Chaudhury
user
cosmik1412
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Soham Dan
65dc073f0af7e21ba4f8c5ef
Maxwell Crouse
claimed_verified
2024-02-28T08:28:24.702Z
65de20ae5522923cf719a744
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Maxwell Crouse
user
mvcrouse
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Asim Munawar
claimed_verified
2024-02-26T14:34:23.811Z
647959323b7f8b1f624c9666
/avatars/f6cad73bcfaa26c6cc4d01727d6a2ba1.svg
Asim Munawar
user
asimmunawar
65dc073f0af7e21ba4f8c5f1
Sadhana Kumaravel
extracted_pending
2024-06-10T17:47:09.321Z
66673a677a87ddaf0f99f002
/avatars/fd26ca86c9a5853a4cd35f68b0e5935d.svg
Sadhana Kumaravel
user
sadhanak
65dc073f0af7e21ba4f8c5f2
Vinod Muthusamy
65dc073f0af7e21ba4f8c5f3
Pavan Kapanipathi
65dc073f0af7e21ba4f8c5f4
Luis A. Lastras
API-BLEND: A Comprehensive Corpora for Training and Benchmarking API
  LLMs
There is a growing need for Large Language Models (LLMs) to effectively use
tools and external Application Programming Interfaces (APIs) to plan and
complete tasks. As such, there is tremendous interest in methods that can
acquire sufficient quantities of train and test data that involve calls to
tools / APIs. Two lines of research have emerged as the predominant strategies
for addressing this challenge. The first has focused on synthetic data
generation techniques, while the second has involved curating task-adjacent
datasets which can be transformed into API / Tool-based tasks. In this paper,
we focus on the task of identifying, curating, and transforming existing
datasets and, in turn, introduce API-BLEND, a large corpora for training and
systematic testing of tool-augmented LLMs. The datasets mimic real-world
scenarios involving API-tasks such as API / tool detection, slot filling, and
sequencing of the detected APIs. We demonstrate the utility of the API-BLEND
dataset for both training and benchmarking purposes.
API-BLEND is introduced as a large dataset for training and benchmarking tool-augmented Large Language Models by simulating real-world API tasks.
Large Language Models LLMs API-tasks API/ tool detection slot filling sequencing of the detected APIs API-BLEND