Papers with enterprise systems
Adapting LLMs for Structured Natural Language API Integration (2024.emnlp-industry)
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Robin Chan, Katsiaryna Mirylenka, Thomas Gschwind, Christoph Miksovic, Paolo Scotton, Enrico Toniato, Abdel Labbi
| Challenge: | API integration is crucial for enterprise systems, but there are challenges in combining APIs based on user intent. |
| Approach: | They propose a framework that leverages large language models to integrate APIs based on natural language input. |
| Outcome: | The proposed framework improves performance over existing methods and RAGs based on open APIs . it can learn structural API constraints implicitly during training and retain structured knowledge . |
Schema Aware Semantic Reasoning for Interpreting Natural Language Queries in Enterprise Settings (2020.coling-main)
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| Challenge: | Using ontology reasoning to understand natural language is a challenge for QA systems . a recent study shows that ontologies can improve natural language understanding . |
| Approach: | They propose to use ontology reasoning to translate natural language interpretation into a sequence of solvable tasks by an ontologist. |
| Outcome: | The proposed framework achieves better natural language understanding with a 30% accuracy improvement over the current state of natural language query interfaces. |
Group, Embed and Reason: A Hybrid LLM and Embedding Framework for Semantic Attribute Alignment (2025.emnlp-industry)
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Shramona Chakraborty, Shashank Mujumdar, Nitin Gupta, Sameep Mehta, Ronen Kat, Itay Etelis, Mohamed Mahameed, Itai Guez, Rachel Tzoref-Brill
| Challenge: | a framework to align attributes that refer to the same concept but differ across schemas is challenging in schema only settings where no instance data is available due to ambiguous names, inconsistent descriptions, and domain-specific terminologies. |
| Approach: | They propose a framework that combines contextual reasoning and embedding-based similarity to address token limitations and hallucinations. |
| Outcome: | The proposed framework scales to large schemas and shows strong performance on healthcare schemas. |
Can LLMs Help You at Work? A Sandbox for Evaluating LLM Agents in Enterprise Environments (2025.emnlp-main)
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| Challenge: | Enterprise systems are crucial for enhancing productivity and strategic growth, but data is fragmented across multiple sources and access controls are complex. |
| Approach: | They propose a benchmark that simulates enterprise settings with 500 diverse tasks . they show that even the most capable models achieve only 41.8% task completion . |
| Outcome: | The proposed benchmark shows that even the most capable models achieve only 41.8% task completion. |