Papers with SLOT
SLOT: Structuring the Output of Large Language Models (2025.emnlp-industry)
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| Challenge: | Structured outputs are essential for large language models (LLMs) but often deviate from predefined schemas hampering reliable application development. |
| Approach: | They propose a model-agnostic approach that transforms unstructured LLM outputs into precise structured formats. |
| Outcome: | The proposed model-agnostic approach transforms unstructured LLM outputs into precise structured formats. |
Live API-Bench: 2500+ Live APIs for Testing Multi-Step Tool Calling (2026.eacl-long)
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Benjamin Elder, Anupama Murthi, Jungkoo Kang, Ankita Rajaram Naik, Kiran Kate, Kinjal Basu, Danish Contractor
| Challenge: | Large language models rely on external tools and APIs to perform tasks specified in natural language. |
| Approach: | They propose a benchmark that transforms SQL queries from BIRD-SQL into executable API sequences. |
| Outcome: | The proposed benchmark evaluates 10 LLMs and 4 ReACT agents with low task completion rates and 50% task completion rate. |
Self-Reflective Generation at Test Time (2026.acl-long)
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| Challenge: | Existing self-reflection mechanisms are reactive and inefficient for large language models . a fundamental tension persists between the ability to execute complex multi-step reasoning and the ability of the model to generate coherent traces. |
| Approach: | They propose a test-time framework that reflects before generating at uncertain points . SRGen utilizes dynamic entropy thresholding to identify high-uncertainty tokens . |
| Outcome: | The proposed framework can significantly strengthen large language models' reasoning process. |