Evaluating Large Language Models with Human Feedback: Establishing a Swedish Benchmark
- URL: http://arxiv.org/abs/2405.14006v1
- Date: Wed, 22 May 2024 21:22:51 GMT
- Title: Evaluating Large Language Models with Human Feedback: Establishing a Swedish Benchmark
- Authors: Birger Moell,
- Abstract summary: Large language models (LLMs) have demonstrated significant capabilities across numerous applications.
This study introduces a comprehensive human benchmark to assess the efficacy of prominent LLMs in understanding and generating Swedish language texts.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In the rapidly evolving field of artificial intelligence, large language models (LLMs) have demonstrated significant capabilities across numerous applications. However, the performance of these models in languages with fewer resources, such as Swedish, remains under-explored. This study introduces a comprehensive human benchmark to assess the efficacy of prominent LLMs in understanding and generating Swedish language texts using forced choice ranking. We employ a modified version of the ChatbotArena benchmark, incorporating human feedback to evaluate eleven different models, including GPT-4, GPT-3.5, various Claude and Llama models, and bespoke models like Dolphin-2.9-llama3b-8b-flashback and BeagleCatMunin. These models were chosen based on their performance on LMSYS chatbot arena and the Scandeval benchmarks. We release the chatbotarena.se benchmark as a tool to improve our understanding of language model performance in Swedish with the hopes that it will be widely used. We aim to create a leaderboard once sufficient data has been collected and analysed.
Related papers
- ML-SUPERB 2.0: Benchmarking Multilingual Speech Models Across Modeling Constraints, Languages, and Datasets [106.7760874400261]
This paper presents ML-SUPERB2.0, which is a new benchmark for evaluating pre-trained SSL and supervised speech models.
We find performance improvements over the setup of ML-SUPERB, but performance depends on the downstream model design.
Also, we find large performance differences between languages and datasets, suggesting the need for more targeted approaches.
arXiv Detail & Related papers (2024-06-12T21:01:26Z) - Benchmarking Pre-trained Large Language Models' Potential Across Urdu NLP tasks [0.9786690381850356]
Large Language Models (LLMs) pre-trained on multilingual data have revolutionized natural language processing research.
This study presents an in-depth examination of prominent LLMs, across 14 tasks using 15 Urdu datasets.
Experiments show that SOTA models surpass all the encoder-decoder pre-trained language models in all Urdu NLP tasks with zero-shot learning.
arXiv Detail & Related papers (2024-05-24T11:30:37Z) - YAYI 2: Multilingual Open-Source Large Language Models [53.92832054643197]
We propose YAYI 2, including both base and chat models, with 30 billion parameters.
YAYI 2 is pre-trained from scratch on a multilingual corpus which contains 2.65 trillion tokens filtered by our pre-training data processing pipeline.
The base model is aligned with human values through supervised fine-tuning with millions of instructions and reinforcement learning from human feedback.
arXiv Detail & Related papers (2023-12-22T17:34:47Z) - IndicSUPERB: A Speech Processing Universal Performance Benchmark for
Indian languages [16.121708272597154]
We release the IndicSUPERB benchmark for speech recognition in 12 Indian languages.
We train and evaluate different self-supervised models alongside a commonly used baseline benchmark.
We show that language-specific fine-tuned models are more accurate than baseline on most of the tasks.
arXiv Detail & Related papers (2022-08-24T20:14:52Z) - Training Language Models with Natural Language Feedback [51.36137482891037]
We learn from language feedback on model outputs using a three-step learning algorithm.
In synthetic experiments, we first evaluate whether language models accurately incorporate feedback to produce refinements.
Using only 100 samples of human-written feedback, our learning algorithm finetunes a GPT-3 model to roughly human-level summarization.
arXiv Detail & Related papers (2022-04-29T15:06:58Z) - PaLM: Scaling Language Modeling with Pathways [180.69584031908113]
We trained a 540-billion parameter, densely activated, Transformer language model, which we call Pathways Language Model PaLM.
We trained PaLM on 6144 TPU v4 chips using Pathways, a new ML system which enables highly efficient training across multiple TPU Pods.
We demonstrate continued benefits of scaling by achieving state-of-the-art few-shot learning results on hundreds of language understanding and generation benchmarks.
arXiv Detail & Related papers (2022-04-05T16:11:45Z) - Scaling Language Models: Methods, Analysis & Insights from Training
Gopher [83.98181046650664]
We present an analysis of Transformer-based language model performance across a wide range of model scales.
Gains from scale are largest in areas such as reading comprehension, fact-checking, and the identification of toxic language.
We discuss the application of language models to AI safety and the mitigation of downstream harms.
arXiv Detail & Related papers (2021-12-08T19:41:47Z) - Predicting the Performance of Multilingual NLP Models [16.250791929966685]
This paper proposes an alternate solution for evaluating a model across languages which make use of the existing performance scores of the model on languages that a particular task has test sets for.
We train a predictor on these performance scores and use this predictor to predict the model's performance in different evaluation settings.
Our results show that our method is effective in filling the gaps in the evaluation for an existing set of languages, but might require additional improvements if we want it to generalize to unseen languages.
arXiv Detail & Related papers (2021-10-17T17:36:53Z) - Sm{\aa}prat: DialoGPT for Natural Language Generation of Swedish
Dialogue by Transfer Learning [1.6111818380407035]
State-of-the-art models for the generation of natural language dialogue have demonstrated impressive performance in simulating human-like, single-turn conversations in English.
This work investigates, by an empirical study, the potential for transfer learning of such models to Swedish language.
arXiv Detail & Related papers (2021-10-12T18:46:43Z) - Comparison of Interactive Knowledge Base Spelling Correction Models for
Low-Resource Languages [81.90356787324481]
Spelling normalization for low resource languages is a challenging task because the patterns are hard to predict.
This work shows a comparison of a neural model and character language models with varying amounts on target language data.
Our usage scenario is interactive correction with nearly zero amounts of training examples, improving models as more data is collected.
arXiv Detail & Related papers (2020-10-20T17:31:07Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.