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..
Layer-Wise Representation Fusion for Compositional Generalization
Authors: Yafang Zheng, Lei Lin, Shuangtao Li, Yuxuan Yuan, Zhaohong Lai, Shan Liu, Biao Fu, Yidong Chen, Xiaodong Shi
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | LRF achieves promising results on two realistic benchmarks, empirically demonstrating the effectiveness of our proposal. |
| Researcher Affiliation | Collaboration | 1Department of Artificial Intelligence, School of Informatics, Xiamen University 2 Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan (Xiamen University), Ministry of Culture and Tourism, China 3Kuaishou Technology, Beijing, China |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Codes are available at https://github.com/thinkaboutzero/LRF. |
| Open Datasets | Yes | CoGnition is an English Chinese (En-Zh) translation dataset...CFQ is automatically generated from a set of rules...Both are cited: "(Li et al. 2021)" and "(Keysers et al. 2020)". |
| Dataset Splits | Yes | CoGnition...It consists of a training set of 196,246 sentence pairs, a validation set and a test set of 10,000 samples." and "CFQ...Each split dataset consists of a training set of 95,743, a validation set and a test set of 11,968 examples. |
| Hardware Specification | Yes | We use one GeForce GTX 2080Ti for training with 100,000 steps and decoding. |
| Software Dependencies | No | The paper mentions software like Fairseq, Jieba, Moses tokenizer, GPT2BPE tokenizer, and RoBERTa but does not provide specific version numbers for any of them. |
| Experiment Setup | Yes | For CoGnition...training with 100,000 steps and decoding. For CFQ...base RoBERTa with 12 encoder layers, which is combined with a Transformer decoder that has 2 decoder layers with hidden size 256 and feed-forward dimension 512...training with 45,000 steps and decoding. |