Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Natural Language Decomposition and Interpretation of Complex Utterances
Authors: Harsh Jhamtani, Hao Fang, Patrick Xia, Eran Levy, Jacob Andreas, Benjamin Van Durme
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that the proposed approach enables the interpretation of complex utterances with almost no complex training data, while outperforming standard few-shot prompting approaches. |
| Researcher Affiliation | Industry | Harsh Jhamtani , Hao Fang , Patrick Xia , Eran Levy , Jacob Andreas and Benjamin Van Durme Microsoft EMAIL |
| Pseudocode | No | The paper does not contain any sections or figures explicitly labeled 'Pseudocode' or 'Algorithm'. |
| Open Source Code | Yes | Code and De CU dataset will be available at https://github.com/microsoft/decomposition-of-complex-utterances |
| Open Datasets | Yes | To study such multi-step complex intent decomposition, we introduce a new dataset we call De CU (Decomposition of Complex Utterances). Code and De CU dataset will be available at https://github.com/microsoft/decomposition-of-complex-utterances |
| Dataset Splits | No | The paper specifies 'ten complex utterances... to be used as training data' and a 'test set consisting of the remaining 200 complex utterances', but it does not explicitly mention a separate validation split. |
| Hardware Specification | No | The paper mentions using 'Open AI s text-davinci-003 model', 'GPT-4 (gpt-4-32k)', and 'LLAMA-2-70B' as the LLMs but does not specify the hardware used to run these models or the experiments. |
| Software Dependencies | No | The paper mentions using specific LLM models like 'text-davinci-003' and 'GPT-4' but does not list other software dependencies with specific version numbers (e.g., programming languages, libraries, frameworks). |
| Experiment Setup | Yes | The model is prompted with K = 10 example decompositions... We use a maximum of M 25 additional elementary utterances... We use Open AI s text-davinci-003 model as the LLM... |