
## Are Emergent Abilities of Large Language Models a Mirage?
Rylan Schaeffer, Brando Miranda, Sanmi Koyejo
Keywords: 
Award: Outstanding Paper
NeurIPS/2023/Proceedings/783 - Are Emergent Abilities of Large Language Models a Mirage?.pdf
Project URL: 

### Implementation
_Given the documentation given by the authors on the method, how much time investment would it be to re-implement the method from scratch?_

[9]

The authors do not share their implementation regarding the analysis conducted. The formulae for analysis are given. 

### Data
_Given the data description in the documentation, how much effort take to either: Find the same dataset the authors used, or similar datasets and defend the comparability, or acquire one from scratch?_

[3]

(1/1)

The authors evaluate LLMs on the BIG-Bench dataset, brief statistics are given in sec 8. Citation provided. No direct links. Description is lacking.  

### Configuration 
_Given the (hyper)parameters, including semantic parameters, of the method: How much effort would it take to acquire the algorithm configurations used for their results, and compare against their budgetary constraints?_

[1]

N/A.

### Experimental Procedure
_Given the experimental set-up of the work, how difficult is it to set up a new experiment, similar to those presented in the original work, with the same procedure?_

[1]

The authors use new metrics and define them in detail. The authors evaluate single models over the datasets. Split not applicable.

### Expertise
_How much effort would it take to acquire the expertise required to reproduce the work independently relying on the available documentation?_

[4]

Requries expertise in LLMs and the evaluation concepts presented.
