Pith. sign in

REVIEW 3 cited by

Is GPT-3 a Good Data Annotator?

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2212.10450 v2 pith:QH35U777 submitted 2022-12-20 cs.CL

Is GPT-3 a Good Data Annotator?

Bosheng Ding , Chengwei Qin , Linlin Liu , Yew Ken Chia , Shafiq Joty , Boyang Li , Lidong Bing This is my paper
classification cs.CL
keywords datagpt-3annotationannotatortasksmodeloutputperformance
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
Share X Bluesky LinkedIn Reddit HN
read the original abstract

Data annotation is the process of labeling data that could be used to train machine learning models. Having high-quality annotation is crucial, as it allows the model to learn the relationship between the input data and the desired output. GPT-3, a large-scale language model developed by OpenAI, has demonstrated impressive zero- and few-shot performance on a wide range of NLP tasks. It is therefore natural to wonder whether it can be used to effectively annotate data for NLP tasks. In this paper, we evaluate the performance of GPT-3 as a data annotator by comparing it with traditional data annotation methods and analyzing its output on a range of tasks. Through this analysis, we aim to provide insight into the potential of GPT-3 as a general-purpose data annotator in NLP.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Dynamic Emotion and Personality Profiling for Multimodal Deception Detection

    cs.CL 2026-04 unverdicted novelty 6.0

    A new dataset DDEP and reliability-weighted fusion model Rel-DDEP jointly detect deception, emotion, and personality from multimodal data, reporting F1 gains of 2.53%, 2.66%, and 9.30% over baselines.

  2. Learning to Select Visual In-Context Demonstrations

    cs.LG 2026-03 reject novelty 5.0

    A Dueling-DQN agent selects visual in-context demonstrations and outperforms kNN retrieval on objective regression benchmarks but not on subjective preference tasks, per the paper's main table.

  3. Online In-Context Distillation for Low-Resource Vision Language Models

    cs.CV 2025-10 unverdicted novelty 5.0

    Online In-Context Distillation lets small VLMs gain up to 33% performance with as little as 4% teacher annotations by distilling knowledge through dynamic in-context demonstrations at inference.