5 papers
Exploring Performance Variations in Finetuned Translators of Ultra-Low Resource Languages: Do Linguistic Differences Matter?
Isabel Gonçalves, Paulo Cavalin, Claudio Pinhanez
Finetuning pre-trained language models with small amounts of data is a commonly-used method to create translators for ultra-low resource languages such as endangered Indigenous lan…
CAT: A Metric-Driven Framework for Analyzing the Consistency-Accuracy Relation of LLMs under Controlled Input Variations
Paulo Cavalin, Cassia Sanctos, Marcelo Grave +2
We introduce \textsc{CAT}, a framework designed to evaluate and visualize the \emph{interplay} of \emph{accuracy} and \emph{response consistency} of Large Language Models (LLMs) un…
Improving Score Reliability of Multiple Choice Benchmarks with Consistency Evaluation and Altered Answer Choices
Paulo Cavalin, Cassia Sanctos, Marcelo Grave +2
In this work we present the Consistency-Rebalanced Accuracy (CoRA) metric, improving the reliability of Large Language Model (LLM) scores computed on multiple choice (MC) benchmark…
The Non-Determinism of Small LLMs: Evidence of Low Answer Consistency in Repetition Trials of Standard Multiple-Choice Benchmarks
Claudio Pinhanez, Paulo Cavalin, Cassia Sanctos +2
This work explores the consistency of small LLMs (2B-8B parameters) in answering multiple times the same question. We present a study on known, open-source LLMs responding to 10 re…
Sentence-level Aggregation of Lexical Metrics Correlates Stronger with Human Judgements than Corpus-level Aggregation
Paulo Cavalin, Pedro Henrique Domingues, Claudio Pinhanez
In this paper we show that corpus-level aggregation hinders considerably the capability of lexical metrics to accurately evaluate machine translation (MT) systems. With empirical e…