MEGAnno+: Collaborative Human-LLM Annotation System

Khái niệm cốt lõi

MEGAnno+ advocates for a collaborative approach between humans and Large Language Models (LLMs) to produce reliable and high-quality labels by addressing the limitations of LLMs in understanding complex contexts.

Tóm tắt

MEGAnno+ introduces a collaborative human-LLM annotation system to enhance data labeling efficiency. It highlights the importance of combining human expertise with LLM capabilities to ensure accurate annotations, especially in specialized domains. The system offers effective LLM agent management, robust annotation processes, and exploratory verification by humans.

Thống kê

Large language models (LLMs) can label data faster and cheaper than humans for various NLP tasks.
LLMs may fall short in understanding complex socio-cultural or domain-specific context.
Studies show that LLMs can achieve near-human or better-than-human accuracy in some tasks.
Downstream models trained with LLM-generated labels may outperform directly using an LLM for inference.
LLMs have limitations that necessitate human intervention in the data annotation process.

Trích dẫn

"Despite their prowess, LLMs may fall short in understanding of complex, socio-cultural, or domain-specific context." - MEGAnno+
"Studies show that LLMs can achieve near-human or even better-than-human accuracy in some tasks." - MEGAnno+

Thông tin chi tiết chính được chắt lọc từ

MEGAnno+

by Hannah Kim,K... lúc arxiv.org 02-29-2024

https://arxiv.org/pdf/2402.18050.pdf

Yêu cầu sâu hơn

How can annotation tools accommodate both human annotators and LLM annotators effectively?

In order to accommodate both human annotators and Large Language Models (LLMs) effectively, annotation tools need to be designed with flexibility and adaptability in mind. Here are some key strategies:

Unified Interface: The tool should provide a unified interface that allows seamless interaction for both human annotators and LLMs. This includes features like prompt customization, model selection, and result visualization that cater to the needs of both types of annotators.

Customizable Workflows: Annotation tools should offer customizable workflows that can be tailored to suit the requirements of different tasks and datasets. This flexibility enables users to switch between human annotations and LLM annotations based on task complexity or specific project goals.

Error Handling Mechanisms: Implement robust error handling mechanisms that can address issues specific to LLM annotations, such as timeouts or rate limit violations. Clear feedback loops should be established for users to intervene when necessary.

Metadata Capture: Ensure that the tool captures relevant metadata from LLM annotations, such as confidence scores or token logits, which can aid in decision-making during verification processes by humans.

Verification Interfaces: Provide intuitive verification interfaces where human annotators can review and correct LLM-generated labels efficiently. Features like search queries, sorting options based on confidence levels, and batch verification capabilities enhance the user experience.

By incorporating these elements into annotation tools, it becomes possible to create a harmonious environment where both human annotators' expertise and LLMs' efficiency can be leveraged effectively.

Should commercial LLMs be used cautiously due to potential risks associated with sensitive information?

Yes, caution is warranted when using commercial Large Language Models (LLMs), especially in scenarios involving sensitive information or intellectual property rights. Here are some reasons why careful consideration is essential:

Data Privacy Concerns: Commercial LLMs may have access to proprietary data shared with them for annotation purposes. There is a risk of data leakage if confidential information is exposed or misused during the annotation process.

Model Retraining Risks: Data provided to commercial models could potentially contribute towards retraining these models without explicit consent from the data owners. This raises concerns about how the annotated data might influence future iterations of the model's performance.

3 .Biased Outputs: Commercial models trained on diverse datasets may exhibit biases in their outputs which could inadvertently perpetuate stereotypes or discriminatory practices if not carefully monitored during annotation tasks involving sensitive topics.
To mitigate these risks, it is advisable either mask any confidential information before sharing it with commercial LMMs or consider utilizing in-house models where there is more control over data privacy measures.

How can the design of annotation tasks optimize the performance of both human annotators and LLM annotators?

Optimizing the design of annotation tasks plays a crucial role in enhancing performance for both human annotators and Large Language Models (LLMs). Here are some strategies for task design optimization:
1 .Clear Task Definition: Define clear objectives for each task along with detailed instructions so that both humans and machines understand what needs to be annotated accurately.
2 .Standardized Labeling Schema: Establish a standardized labeling schema consistent across all tasks so that annotations remain uniform regardless of whether they are done by humans or by an LMM.
3 .Prompt Consistency: Design prompts consistently across all instances within a task ensuring coherence between training prompts given earlier versus those presented at inference time.
4 .Task Complexity Gradation: Structure tasks accordingto complexity levels allowing simpler ones suitablefor automated processing while reserving more complex cases requiring nuanced understandingfor manual intervention.
5 .Feedback Loop Integration: Incorporate feedback mechanisms enabling continuous improvement through iterative cycles basedon insights gained from previousannotationsbybothhumansand LLMS alike
By implementing these optimizationsinannotationtaskdesign,theoverallqualityofannotationsgeneratedbybothhumanannotator sandLMMscanbeenhanced,resultinginmoreaccurateand reliable labeleddataoutputsacrossavarietyoftasksandinference scenarios