Language Generation in the Limit
Authors: Jon Kleinberg, Sendhil Mullainathan
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The paper consists entirely of theoretical results. Due to the emphasis on guarantees against an adversary in the limit, there is not a natural opening for demonstrating the results experimentally (as there similarly isn't in the work that introduced the Gold Angluin model). |
| Researcher Affiliation | Academia | Jon Kleinberg Departments of Computer Science and Information Sciene Cornell University Ithaca NY Sendhil Mullainathan Booth School of Business University of Chicago Chicago IL |
| Pseudocode | No | Section 5.1 'An algorithm for generation in the limit' describes the algorithm's steps in prose and bullet points, but it does not present formal pseudocode or a clearly labeled algorithm block in a code-like format. |
| Open Source Code | No | The results of the paper do not make use of data or code. |
| Open Datasets | No | The results of the paper do not make use of data or code. |
| Dataset Splits | No | The paper does not include computational experiments, and therefore does not provide dataset split information. |
| Hardware Specification | No | The paper does not include computational experiments, and therefore does not provide hardware specifications. |
| Software Dependencies | No | The results of the paper do not make use of data or code, and therefore no specific software dependencies are provided for replication of experiments. |
| Experiment Setup | No | The paper does not include computational experiments, and therefore no experimental setup details like hyperparameters or training settings are provided. |