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Found 5 papers, 4 papers with code
When Quantization Affects Confidence of Large Language Models?
Recent studies introduced effective compression techniques for Large Language Models (LLMs) via post-training quantization or low-bit weight representation.
Mini Minds: Exploring Bebeshka and Zlata Baby Models
In this paper, we describe the University of Lyon 2 submission to the Strict-Small track of the BabyLM competition.
Can BERT eat RuCoLA? Topological Data Analysis to Explain
Our results contribute to understanding the behavior of monolingual LMs in the acceptability classification task, provide insights into the functional roles of attention heads, and highlight the advantages of TDA-based approaches for analyzing LMs.
Ranked #1 on
Linguistic Acceptability
on RuCoLA
The Other Side of Compression: Measuring Bias in Pruned Transformers
Social media platforms have become popular worldwide.
Acceptability Judgements via Examining the Topology of Attention Maps
The role of the attention mechanism in encoding linguistic knowledge has received special interest in NLP.
Ranked #1 on
Linguistic Acceptability
on ItaCoLA
