How Many Parameters Does it Take to Change a Light Bulb? Evaluating
  Performance in Self-Play of Conversational Games as a Function of Model
  Characteristics
Authors: name: Nidhir Bhavsar, name: Jonathan Jordan, name: Sherzod Hakimov, name: David Schlangen

What makes a good Large Language Model (LLM)? That it performs well on the
relevant benchmarks -- which hopefully measure, with some validity, the
presence of capabilities that are also challenged in real application. But what
makes the model perform well? What gives a model its abilities? We take a
recently introduced type of benchmark that is meant to challenge capabilities
in a goal-directed, agentive context through self-play of conversational games,
and analyse how performance develops as a function of model characteristics
like number of parameters, or type of training. We find that while there is a
clear relationship between number of parameters and performance, there is still
a wide spread of performance points within a given size bracket, which is to be
accounted for by training parameters such as fine-tuning data quality and
method. From a more practical angle, we also find a certain degree of
unpredictability about performance across access methods, possible due to
unexposed sampling parameters, and a, very welcome, performance stability
against at least moderate weight quantisation during inference.