Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Compositional languages emerge in a neural iterated learning model
Authors: Yi Ren, Shangmin Guo, Matthieu Labeau, Shay B. Cohen, Simon Kirby
ICLR 2020 | Venue PDF | LLM Run Details | Input Tokens: 23,544 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,140 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments confirm our analysis, and also demonstrate that the emerged languages largely improve the generalizing power of the neural agent communication. |
| Researcher Affiliation | Academia | 1 University of Edinburgh, United Kingdom, 2 University of Cambridge, United Kingdom 3 LTCI, T el ecom Paris, Institut Polytechnique de Paris, France |
| Pseudocode | Yes | Algorithm 1: The NIL algorithm. |
| Open Source Code | Yes | The code is available at https://github.com/Joshua-Ren/Neural_Iterated_Learning. |
| Open Datasets | No | The paper describes generating its own object space based on attributes (Na) and values (Nv), rather than using a publicly available dataset. |
| Dataset Splits | Yes | We measure this ability by looking at their validation game performance: we restrict the training examples to a limited numbers of objects (i.e., the training set), and look at how good are the agents at playing the game on the others (i.e., the validation set)." and "Valid set size 0 8 16 32 |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory amounts used for experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | Unless specifically stated, the experiments mentioned in this paper use the hyper-parameters given in Table 3. |