Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline | Paper Citation Record

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This paper cites ReAct: Synergizing Reasoning and Acting in Language Models.

Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline ReAct: Synergizing Reasoning and Acting in Language Models

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Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline The Rise and Potential of Large Language Model Based Agents: A Survey

Reference 2

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Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline TDAG: A Multi-Agent Framework based on Dynamic Task Decomposition and Agent Generation

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Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline Advancing Agentic Systems: Dynamic Task Decomposition, Tool Integration and Evaluation using Novel Metrics and Dataset

Reference 4

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Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline Research of the Enterprise Application Integration Platform Based on Multi-agent

Reference 5

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This paper cites Enterprise Design, Operations and Computing with AI Agents: Accountability using DSL.

Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline Enterprise Design, Operations and Computing with AI Agents: Accountability using DSL

Reference 6

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This paper cites Enhancing Trust in LLM-Based AI Automation Agents: New Considerations and Future Challenges.

Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline Enhancing Trust in LLM-Based AI Automation Agents: New Considerations and Future Challenges

Reference 7

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Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline Scaling instruction-finetuned language models

Reference 8

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Observation 0a69c8dd-1fc2-42a3-ba83-fbacae7769cd · outbound

This paper cites Alpaca: A strong, replicable instruction-following model.

Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline Alpaca: A strong, replicable instruction-following model

Reference 9

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Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline AI and privacy concerns: a smart meter case study

Reference 10

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Observation aa302119-d4e1-4848-a0d3-6c53b1f8e3e4 · outbound

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Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline LoRA: Low-Rank Adaptation of Large Language Models

Reference 11

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Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline Qwen Technical Report

Reference 12

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Observation d59d3aa0-467b-4583-a1ed-f5f813c4c4ef · outbound

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Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline A Survey of Large Language Models

Reference 13

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This paper cites On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey.

Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey

Reference 14

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This paper cites Self-Instruct: Aligning Language Models with Self-Generated Instructions.

Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline Self-Instruct: Aligning Language Models with Self-Generated Instructions

Reference 15

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Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline WizardLM: Empowering large pre-trained language models to follow complex instructions

Reference 16

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Observation 87e2d1a0-5e07-4900-bdc2-242884257852 · outbound

This paper cites Toolformer: Language models can teach themselves to use tools.

Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline Toolformer: Language models can teach themselves to use tools

Reference 17

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Observation 16d268f7-5216-4478-94e9-1e629191c9bd · outbound

This paper cites ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs.

Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs

Reference 18

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This paper cites ToolACE: Winning the Points of LLM Function Calling.

Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline ToolACE: Winning the Points of LLM Function Calling

Reference 19

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This paper cites ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases.

Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases

Reference 20

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Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline APIGen: Automated Pipeline for Generating Verifiable and Diverse Function-Calling Datasets

Reference 21

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Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline Measuring Massive Multitask Language Understanding

Reference 22

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Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline CMMLU: Measuring massive multitask language understanding in Chinese

Reference 23

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Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline C-eval: A multi-level multi-discipline chinese evaluation suite for foundation models

Reference 24

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Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline Training Verifiers to Solve Math Word Problems

Reference 25

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Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering

Reference 26

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Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline OpenCompass: A Universal Evaluation Platform for Foundation Models

Reference 27

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Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline Gorilla: Large Language Model Connected with Massive APIs

Reference 28

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Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline On the Tool Manipulation Capability of Open-source Large Language Models

Reference 29

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Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agents

Reference 30

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Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline https://gorilla.cs.berkeley

Reference 31

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This paper cites Springer Nature, 2019.

Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline Springer Nature, 2019

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Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline Chain-of-thought prompting elicits reasoning in large language models

Reference 33

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Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline Language models are few-shot learners

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Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline AdapterFusion: Non-Destructive Task Composition for Transfer Learning

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Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline Learning to Represent Programs with Graphs

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Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline A systematic analysis of performance measures for classification tasks

Reference 37

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Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline Parameter-efficient transfer learning for NLP

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Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

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Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline Transfer learning in natural language processing

Reference 40

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Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline Scaling Laws for Neural Language Models

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Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline Making Pre-trained Language Models Better Few-shot Learners

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Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning

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Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline Cheap and fast–but is it good? evaluating non-expert annotations for natural language tasks

Reference 44

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Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Reference 45

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Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline Methodologies for data quality assessment and improvement

Reference 46

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Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline Beyond accuracy: What data quality means to data consumers

Reference 47

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Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline Don't Stop Pretraining: Adapt Language Models to Domains and Tasks

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Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline The Pile: An 800GB Dataset of Diverse Text for Language Modeling

Reference 49

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Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline Training language models to follow instructions with human feedback

Reference 50

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Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline Exploring the limits of transfer learning with a unified text-to-text trans- former

Reference 51

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This paper cites Smaller, Weaker, Yet Better: Training LLM Reasoners via Compute-Optimal Sampling.

Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline Smaller, Weaker, Yet Better: Training LLM Reasoners via Compute-Optimal Sampling

Reference 52

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Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline Climbing towards NLU: On meaning, form, and understanding in the age of data

Reference 53

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Observation 6cbb6709-67dd-4eb5-a4c8-8c386bdb3fc6 · outbound

This paper cites LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models.

Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models

Reference 54

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Observation 104d8fb5-d5c6-4d14-a6f9-5f1d10d0e9c6 · outbound

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Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline Universal Language Model Fine-tuning for Text Classification

Reference 55

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Observation 778d01d7-fce1-46ce-8f28-918c12b4fe40 · outbound

This paper cites A study of cross-validation and bootstrap for accuracy estimation and model selection.

Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline A study of cross-validation and bootstrap for accuracy estimation and model selection

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:17:29.123250Z

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Observation 2d4439d9-ce57-479f-860f-da19246f1f07 · outbound

This paper cites How to fine-tune bert for text classification?.

Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline How to fine-tune bert for text classification?

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:17:29.098189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d8c650e4-39a5-4c5f-96f7-7df10fc670e5 · outbound

This paper cites Attention is all you need.

Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline Attention is all you need

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:17:29.058952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f6b042c3-f4c1-448c-861b-df0af209ea3e · outbound

This paper cites Dropout: a simple way to prevent neural networks from overfitting.

Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline Dropout: a simple way to prevent neural networks from overfitting

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:17:29.030269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0bf13003-390f-4074-8d6c-3c6cefaa69d6 · outbound

This paper cites ZeRO: Memory Optimizations Toward Training Trillion Parameter Models.

Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline ZeRO: Memory Optimizations Toward Training Trillion Parameter Models

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:17:28.993743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 28959582-3794-4640-81f5-969ed2ff4f3f · outbound

This paper cites Model merging with SVD to tie the Knots.

Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline Model merging with SVD to tie the Knots

Reference 61

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Observation 4c1c092e-529b-45e9-a378-7135a9467c4e · outbound

This paper cites PEFT: State-of-the-art Parameter-Efficient Fine-Tuning methods.

Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline PEFT: State-of-the-art Parameter-Efficient Fine-Tuning methods

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:17:28.965800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation cff66ea7-e3b7-469f-bae8-718908836c8c · outbound

This paper cites Compilers: Principles, techniques and tools, 2nd editio.

Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline Compilers: Principles, techniques and tools, 2nd editio

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:17:28.934617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ad03b46e-a6a3-4f94-9871-61ac169a39e7 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline Evaluating Large Language Models Trained on Code

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-11T11:17:27.436795Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 11c938d6-411c-4bf9-8723-1883b32d9f82 · outbound

This paper cites Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation.

Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-11T11:17:27.448017Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a6faa5a5-3c43-499f-885c-99658b635da5 · outbound

This paper cites Direct preference optimization: Your language model is secretly a reward model.

Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline Direct preference optimization: Your language model is secretly a reward model

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:17:28.876190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c206fdbf-f07b-436c-8487-ae7e4a3bcd5f · outbound

This paper cites "" Appendix A.2 Question Generation with real name prompt = f.

Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline "" Appendix A.2 Question Generation with real name prompt = f

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:17:28.845685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Reference resolution

Outbound references

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