Task Contamination: Language Models May Not Be Few-Shot Anymore
- URL: http://arxiv.org/abs/2312.16337v1
- Date: Tue, 26 Dec 2023 21:17:46 GMT
- Title: Task Contamination: Language Models May Not Be Few-Shot Anymore
- Authors: Changmao Li and Jeffrey Flanigan
- Abstract summary: Large language models (LLMs) offer impressive performance in various zero-shot and few-shot tasks.
However, their success in zero-shot and few-shot settings may be affected by task contamination.
This paper investigates how zero-shot and few-shot performance of LLMs has changed chronologically over time.
- Score: 9.696290050028237
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Large language models (LLMs) offer impressive performance in various
zero-shot and few-shot tasks. However, their success in zero-shot and few-shot
settings may be affected by task contamination, a potential limitation that has
not been thoroughly examined. This paper investigates how zero-shot and
few-shot performance of LLMs has changed chronologically over time. Utilizing
GPT-3 series models and several other recent open-sourced LLMs, and controlling
for dataset difficulty, we find that on datasets released before the LLM
training data creation date, LLMs perform surprisingly better than on datasets
released after. This strongly indicates that, for many LLMs, there exists task
contamination on zero-shot and few-shot evaluation for datasets released prior
to the LLMs' training data creation date. Additionally, we utilize training
data inspection, task example extraction, and a membership inference attack,
which reveal further evidence of task contamination. Importantly, we find that
for classification tasks with no possibility of task contamination, LLMs rarely
demonstrate statistically significant improvements over simple majority
baselines, in both zero and few-shot settings.
Related papers
- Large Language Models are Few-shot Multivariate Time Series Classifiers [23.045734479292356]
Large Language Models (LLMs) have been extensively applied in time series analysis.
Yet, their utility in the few-shot classification (i.e., a crucial training scenario) is underexplored.
We aim to leverage the extensive pre-trained knowledge in LLMs to overcome the data scarcity problem.
arXiv Detail & Related papers (2025-01-30T03:59:59Z) - 60 Data Points are Sufficient to Fine-Tune LLMs for Question-Answering [50.12622877002846]
Large language models (LLMs) encode extensive world knowledge through pre-training on massive datasets, which can be fine-tuned for the question-answering (QA) task.
We categorize supervised fine-tuning (SFT) data based on the extent of knowledge memorized by the pretrained LLMs.
Our experiments show that as few as 60 data points during the SFT stage can activate the knowledge encoded during pre-training, enabling LLMs to perform the QA task.
arXiv Detail & Related papers (2024-09-24T07:38:38Z) - Improving the Ability of Pre-trained Language Model by Imparting Large Language Model's Experience [4.814313782484443]
Large Language Models (LLMs) and pre-trained Language Models (LMs) have achieved impressive success on many software engineering tasks.
We use LLMs to generate domain-specific data, thereby improving the performance of pre-trained LMs on the target tasks.
arXiv Detail & Related papers (2024-08-16T06:37:59Z) - SELF-GUIDE: Better Task-Specific Instruction Following via Self-Synthetic Finetuning [70.21358720599821]
Large language models (LLMs) hold the promise of solving diverse tasks when provided with appropriate natural language prompts.
We propose SELF-GUIDE, a multi-stage mechanism in which we synthesize task-specific input-output pairs from the student LLM.
We report an absolute improvement of approximately 15% for classification tasks and 18% for generation tasks in the benchmark's metrics.
arXiv Detail & Related papers (2024-07-16T04:41:58Z) - Are you still on track!? Catching LLM Task Drift with Activations [55.75645403965326]
Task drift allows attackers to exfiltrate data or influence the LLM's output for other users.
We show that a simple linear classifier can detect drift with near-perfect ROC AUC on an out-of-distribution test set.
We observe that this approach generalizes surprisingly well to unseen task domains, such as prompt injections, jailbreaks, and malicious instructions.
arXiv Detail & Related papers (2024-06-02T16:53:21Z) - Elephants Never Forget: Memorization and Learning of Tabular Data in Large Language Models [21.10890310571397]
Large Language Models (LLMs) can be applied to a diverse set of tasks, but the critical issues of data contamination and memorization are often glossed over.
This work introduces a variety of different techniques to assess whether a language model has seen a dataset during training.
We then compare the few-shot learning performance of LLMs on datasets that were seen during training to the performance on datasets released after training.
arXiv Detail & Related papers (2024-04-09T10:58:21Z) - How Much are Large Language Models Contaminated? A Comprehensive Survey and the LLMSanitize Library [68.10605098856087]
Large Language Models (LLMs) are increasingly being used in business applications and fundraising in AI.
LLMs' performance may not be reliable anymore, as the high performance may be at least partly due to their previous exposure to the data.
We release an open-source Python library named LLMSanitize implementing major contamination detection algorithms.
arXiv Detail & Related papers (2024-03-31T14:32:02Z) - LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement [79.31084387589968]
Pretrained large language models (LLMs) are currently state-of-the-art for solving the vast majority of natural language processing tasks.
We propose LLM2LLM, a data augmentation strategy that uses a teacher LLM to enhance a small seed dataset.
We achieve improvements up to 24.2% on the GSM8K dataset, 32.6% on CaseHOLD, 32.0% on SNIPS, 52.6% on TREC and 39.8% on SST-2 over regular fine-tuning in the low-data regime.
arXiv Detail & Related papers (2024-03-22T08:57:07Z) - Elephants Never Forget: Testing Language Models for Memorization of
Tabular Data [21.912611415307644]
Large Language Models (LLMs) can be applied to a diverse set of tasks, but the critical issues of data contamination and memorization are often glossed over.
We introduce a variety of different techniques to assess the degrees of contamination, including statistical tests for conditional distribution modeling and four tests that identify memorization.
arXiv Detail & Related papers (2024-03-11T12:07:13Z) - TRACE: A Comprehensive Benchmark for Continual Learning in Large
Language Models [52.734140807634624]
Aligned large language models (LLMs) demonstrate exceptional capabilities in task-solving, following instructions, and ensuring safety.
Existing continual learning benchmarks lack sufficient challenge for leading aligned LLMs.
We introduce TRACE, a novel benchmark designed to evaluate continual learning in LLMs.
arXiv Detail & Related papers (2023-10-10T16:38:49Z)
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.