# Better Question-Answering Models on a Budget

Source: [Better Question-Answering Models on a Budget](<https://www.sota2.com/research/paper/68fa6f211e209131cea43e94>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:56:58.537Z

Authors: Yudhanjaya Wijeratne, Ishan Marikar

Published: 2023-04-24T18:06:27.000Z

## Abstract

Low-rank adaptation (LoRA) and question-answer datasets from large language models have made it much easier for much smaller models to be finetuned to the point where they display sophisticated conversational abilities. In this paper, we present Eluwa, a family of LoRA models that use the Stanford Alpaca dataset and massively improve the capabilities of Facebook's OPT 1.3B, 2.7B and 6.7B models. We benchmark these models in multiple ways, including letting GPT-4 judge their answers to prompts that span general knowledge, writing, programming and other tasks. We show that smaller models here can be fine-tuned to be as performant as models 3x larger - all for as little as 40 USD in compute.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2304.12370>)

[PDF](<https://arxiv.org/pdf/2304.12370.pdf>)