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 Inference in Context – Investigating Contextual Reasoning over Long Texts
Authors: Hanmeng Liu, Leyang Cui, Jian Liu, Yue Zhang13388-13396
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results show that state-of-the-art language models perform by far worse than educated humans. Our dataset can also serve as a testing-set for downstream tasks like checking the factual correctness of summaries. We evaluate the state-of-the-art NLI models to establish baseline performances for Con TRo L. Experimental results demonstrate a significant gap between machine and human ceiling performance. |
| Researcher Affiliation | Academia | 1 Zhejiang University, 2 Fudan University, 3 Westlake University |
| Pseudocode | No | The paper describes model structures and implementation details but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | Our dataset and results are released at https://github.com/csitfun/ConTRoL-dataset. (The link points to the dataset and results, not explicitly the source code for the methodology.) |
| Open Datasets | Yes | Our dataset and results are released at https://github.com/csitfun/ConTRoL-dataset. |
| Dataset Splits | Yes | We randomly split the dataset into training, development, and test set with the ratio of 8:1:1. |
| Hardware Specification | No | The paper mentions Transformer-based models and their token limits but does not provide specific hardware details like GPU/CPU models or processor types used for experiments. |
| Software Dependencies | No | The paper mentions models like BERT, RoBERTa, Longformer, and BART but does not provide specific version numbers for software dependencies or libraries used in the implementation. |
| Experiment Setup | Yes | All models are trained for 10 epochs. We find hyper-parameters using grid search: batch size {8, 16, 32} learning rate {1e 5, 2e 5, 3e 5, 4e 5, 5e 5} and gradient accumulate step {1, 2, 4}. We set the max length to 512 tokens for all models except Longformer, of which 3,000 tokens are the max length we take. |