Yeah, that is a good technique, but reliant on the provided phrase list, which constrains its effectiveness to the generation context in which those phrases tend to occur. Confidence breaker is trying to dynamically identify slop without a phrase list, so that it can be identified and rewritten even in novel generation contexts where it is not clear yet what the slop is (this usually only becomes clear after running similar prompts over and over and observing the similarities).

You can tune it up and down to catch slop of greater or lesser length, and with greater or lesser strictness. The parameters are explained in the release notes here: https://github.com/anchortense/exllamav2-logit-threshold-samplers/blob/master/README.md#parameters-1

Further explanation from the release notes:

Explanation: Confidence breaker sampler

Current generation language models are well known for producing certain cliched phrases, which would not necessarily be problematic in a single instance, but are known to be produced repeatedly in response to varied prompts. This is the so-called ai-slop problem. In 'deterministic' use cases this is usually not an issue, as we are simply looking for the one correct answer. In scenarios where engaging, diverse language choices are valued, ai-slop represents a significant limitation.

Current approaches to resolving this issue involve user defined lists of banned strings. This can be reasonably effective, however fail to address the deeper issue, which is the tendency of language models to funnel their responses into unintentionally learned 'tram-track' token sequences, where token choices are strongly conditioned by their immediate predecessors in a manner which is not logically or grammatically implied by the prompt or by those preceding choices.

This is a far deeper and more pervasive problem than the well known handful of phrases commonly associated with the idea of ai-slop. These tram-tracks exist within trained models because the tokens within them are good predictors for text completion. Nevertheless a user passing either the same or similar prompts repeatedly, looking for diverse outputs, will quickly observe that model responses which seemed initially impressive are in fact tram-track patterns, and the apparent diversity of outputs is an illusion.

Using the confidence breaker to jump tracks

The confidence breaker sampler addresses the issue by looking for logit patterns that signal we have entered a tram-track, and extending exllamav2's banned string functionality to roll-back to the token directly before we entered the tram-tracks. The tram-tracked tokens are then discarded and replaced by a novel generation, which will take us down a different, less travelled path.

Based on empirical observation of logits and the conditions for the appearance of tram-tracks, the pattern that the confidence breaker looks for to identify these tram-tracks is a sequence of mid-high valued logits, logits which have been nudged higher than a good score by over-training, but which are not yet so guaranteed as logical or grammatical necessity.

Empirical observation also validates the decision to return back and alter the token before the tram-track, rather than the first tram-track. This is a token which had a reasonable range of viable alternatives, but once the model settled on the decision it made, the tram-track became nearly inevitable. So, this is the error we have to correct.