Fine-tuning vs Prompting: Understanding Human Values in Language Models

核心概念

Language models can understand human values through fine-tuning and prompting techniques.

要約

Accurately handling human values in sentences is crucial for understanding tendencies. The article explores fine-tuning and prompt tuning using the Human Value Detection 2023 dataset. Existing datasets have limitations, leading to the proposal of Touch´e23-ValueEval with diverse arguments. Most teams tried classification methods, but top performance remains at an average F1 score of 0.56. The project focuses on comparing prompt-tuning with fine-tuning and evaluating PLMs' capabilities aligned with RLHF.

統計

Most participating teams achieved an average F1 score of 0.56.
RoBERTaLarge w/ MH achieved a macro F1 score of 0.522 after contrastive learning fine-tune.
T5-OA had an F1 score of 0.55 for Benevolence: caring.
GPT2†-BCA scored 0.42 for Stimulation.
Llama-7B reached an F1 score of 0.71 for Universalism: tolerance.

引用

"Prompt tuning leverages knowledge acquired during pre-training, showing excellent performance on few-shot tasks."
"Fine-tuning has been widely explored post BERT release, achieving impressive results but struggling in few-shot scenarios."
"LLMs demonstrate remarkable reasoning capabilities, particularly in systematic prompting processes."

抽出されたキーインサイト

Fine-tuning vs Prompting, Can Language Models Understand Human Values?

by Pingwei Sun 場所 arxiv.org 03-18-2024

https://arxiv.org/pdf/2403.09720.pdf

深掘り質問

How can prompt tuning enhance generalization compared to direct fine-tuning?

Prompt tuning can enhance generalization compared to direct fine-tuning by leveraging the knowledge acquired during pre-training more effectively. When using prompts, language models are guided to generate responses based on specific templates or questions, which helps in focusing the model's attention on relevant information for the task at hand. This approach allows for a more structured and controlled way of utilizing the model's capabilities without extensive retraining.
Additionally, prompt tuning reduces the need for extensive fine-tuning on large datasets by incorporating prior knowledge directly into the prompting process. By providing tailored prompts that align with the task requirements, language models can make better use of their pre-existing understanding and reasoning abilities. This results in improved performance on downstream tasks while requiring fewer task-specific examples for training.
In essence, prompt tuning enhances generalization by enabling language models to adapt quickly to new tasks through targeted prompts that guide their decision-making processes without relying solely on vast amounts of labeled data for fine-tuning.

What are the implications of inconsistencies between external knowledge and task definitions on LLMs' performance?

Inconsistencies between external knowledge sources such as ChatGPT-generated information and predefined task definitions can significantly impact LLMs' performance in detecting human values. These inconsistencies may lead to misinterpretations or incorrect mappings between generated outputs from external sources and expected labels within a given context.
When there is a mismatch between external knowledge provided during prompting and actual task requirements, LLMs may struggle to accurately identify human values within arguments. This discrepancy could result in erroneous classifications or misalignments with ground truth annotations, leading to decreased overall performance metrics like F1 scores or accuracy rates.
Furthermore, inconsistent external knowledge may introduce noise or bias into LLMs' decision-making processes, affecting their ability to generalize well across different scenarios. It could also hinder interpretability and trustworthiness if models rely heavily on unreliable or conflicting information sources during inference.
To mitigate these implications, it is crucial to ensure that external knowledge aligns closely with defined task objectives and that any discrepancies are addressed through careful validation procedures before integrating additional information into LLM-based systems.

How can language models be further improved to reduce false positives in human values detection?

Reducing false positives in human values detection using language models involves several strategies aimed at enhancing model precision and minimizing errors in classification outcomes:

Fine-Tuning Strategies: Implement advanced fine-tuning techniques like contrastive learning loss functions tailored specifically for multi-label classification tasks related to human values detection.

Optimized Prompt Tuning: Refine prompt templates used during training by incorporating knowledgeable verbalizers that map output results accurately back to predefined value categories.

Enhanced Knowledge Integration: Integrate domain-specific synonyms or expanded descriptions derived from additional resources (e.g., ChatGPT) into prompting mechanisms for better alignment with nuanced value distinctions.

Structured Questioning Approaches: Develop Chain-of-Thought (CoT) templates designed explicitly for logical inference tasks involving complex concepts like human values; this structured approach guides LLMs towards extracting pertinent details efficiently.

Validation Mechanisms: Implement rigorous validation protocols ensuring consistency between input features (e.g., premise-conclusion pairs) containing implicit value indicators and corresponding label predictions made by the model; this helps minimize false positive detections caused by ambiguous inputs.

By combining these approaches along with continuous evaluation against high-quality annotated datasets representing diverse cultural perspectives, language models can be refined iteratively towards achieving higher accuracy rates while reducing false positives in identifying human values within textual content effectively