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..
NELLIE: A Neuro-Symbolic Inference Engine for Grounded, Compositional, and Explainable Reasoning
Authors: Nathaniel Weir, Peter Clark, Benjamin Van Durme
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In experiments, NELLIE outperforms a similar-sized stateof-the-art reasoner while producing knowledgegrounded explanations. We also find NELLIE can exploit both semi-structured and NL text corpora to guide reasoning. |
| Researcher Affiliation | Collaboration | Nathaniel Weir1, Peter Clark2, and Benjamin Van Durme1 1Johns Hopkins University, Baltimore, MD, USA 2Allen Institute for AI, Seattle, WA, USA |
| Pseudocode | Yes | Full pseudocode for the algorithm, which follows a depth-first search with a breadth-first lookahead [Stern et al., 2010] to check for the unification of generated subgoals, can be found in E. |
| Open Source Code | Yes | 1Code and appendix at https://github.com/JHU-CLSP/NELLIE. |
| Open Datasets | Yes | In our experiments, we consider one implementation of this framework that uses the corpus World Tree [Xie et al., 2020], a set of 9K NL science facts... We evaluate models on two multiple-choice QA datasets constructed so that correct answers are supported by facts in the World Tree corpus: Entailment Bank [Dalvi et al., 2021]... World Tree [Xie et al., 2020]... We consider Open Book QA [Mihaylov et al., 2018]... |
| Dataset Splits | No | The paper mentions using specific datasets (Entailment Bank, World Tree QA, Open Book QA) and refers to a 'test set' for evaluation. However, it does not explicitly provide the training, validation, and test dataset splits with percentages or sample counts for its own experiments in the main text. |
| Hardware Specification | No | The paper mentions models like 'T5-3B model' but does not provide any specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions using a 'T5-3B model' and a 'FAISS [Johnson et al., 2019]-based nearest neighbor dense retrieval index', as well as Prolog. However, no specific version numbers are provided for these software components or any other libraries or frameworks. |
| Experiment Setup | Yes | It is parameterized by 1. A maximum number of proofs m at which to cut off searching. In experiments, we set this to 10 for top-level queries and 2 for recursive subqueries. 2. A number of support facts nf to retrieve at each call to RETRIEVEK, which we set to 15. 3. Candidate generation rates nv for vanilla nucleussampled decompositions, nt for template-conditioned decompositions, and nr for retrieval-conditioned generations. We set these each to 40. ... NELLIE searches for up to p=10 proofs of max depth d=5 with a timeout of t=180 seconds per option. |