Self-Refinement Algorithm for Language Models Using Proxy Metrics Feedback

Konsep Inti

The author introduces the ProMiSe algorithm, enabling language models to refine their responses based on external proxy metrics feedback, improving response quality iteratively.

Abstrak

The ProMiSe algorithm allows language models to self-refine responses using external metrics feedback. It is applied to content-grounded question answering and multi-turn dialogue generation, showing significant improvements in response quality. The algorithm combines proxy metric thresholding with principle-specific refinement, enhancing the alignment of responses with desired principles.
Key points:

Introduction of ProMiSe for self-refinement in language models.
Application of ProMiSe to improve response quality in document-grounded question answering.
Utilization of proxy metrics for iterative refinement based on user-defined principles.
Evaluation on MultiDoc2Dial and QuAC datasets showcasing effectiveness in both few-shot learning and supervised fine-tuning settings.

Statistik

ROUGE-L between response and document: 21.55 (initial) - 21.72 (final)
BERT-Recall: 28.11 (initial) - 29.29 (final)
Recall: 40.42 (initial) - 42.74 (final)
BERT K-Precision: 32.34 (initial) - 34.14 (final)
K-Precision: 76.77 (initial) - 79.29 (final)

Kutipan

"We introduce a novel domain-agnostic algorithm, ProMiSe, to perform multi-aspect self-refinement on desirable principles for a response through in-context learning."
"ProMiSe leverages feedback on response quality through principle-specific proxy metrics, yielding an overall better final response."

Wawasan Utama Disaring Dari

Self-Refinement of Language Models from External Proxy Metrics Feedback

by Kesh... pada arxiv.org 03-05-2024

https://arxiv.org/pdf/2403.00827.pdf

Pertanyaan yang Lebih Dalam

How can the ProMiSe algorithm be adapted for use in other tasks beyond question answering?

The ProMiSe algorithm can be adapted for use in various tasks beyond question answering by customizing the set of principles and proxy metrics to align with the specific objectives of the task at hand. For instance, in text summarization, principles such as conciseness, coherence, and informativeness could guide the refinement process. The proxy metrics would then evaluate aspects like overlap with reference summaries and coherence within the generated text. Similarly, in dialogue generation tasks, principles related to maintaining context consistency, naturalness of responses, and engagement could drive the self-refinement process.
By defining task-specific principles and corresponding proxy metrics tailored to different domains or applications, ProMiSe can effectively guide language models towards generating high-quality outputs that meet desired criteria across a wide range of tasks.

What potential biases or ethical concerns should be considered when implementing self-refinement algorithms like ProMiSe?

When implementing self-refinement algorithms like ProMiSe, several potential biases and ethical concerns need to be carefully considered:

Bias Amplification: If the initial data used for training contains biases or stereotypes, there is a risk that these biases may get amplified during self-refinement iterations.

Adversarial Manipulation: Adversaries could exploit self-refinement mechanisms to generate toxic or harmful content by selecting adversarial principles that lead to undesirable outcomes.

Lack of Diversity: Without proper oversight and diverse input sources for feedback (both human and automated), there is a risk of reinforcing narrow perspectives or limiting creativity in response generation.

Privacy Concerns: In scenarios where user data is involved in providing feedback on model responses (e.g., chat logs), privacy considerations must be taken into account to protect sensitive information from being exposed through iterative refinements.

Transparency & Explainability: It's essential to ensure transparency in how decisions are made during refinement iterations so that users understand why certain changes are being made by the model.

Addressing these concerns requires careful monitoring, regular audits of model behavior, diverse training data sources representing various demographics and viewpoints, as well as robust mechanisms for bias detection and mitigation throughout the self-refinement process.

How might integration of human evaluation alongside automated metrics impact assessment of language model performance?

The integration of human evaluation alongside automated metrics can provide valuable insights into language model performance by offering complementary perspectives:

Subjectivity vs Objectivity: Human evaluations capture subjective aspects such as fluency,
engagement levels which cannot always be quantified using automated metrics alone.

Real-World Relevance: Human evaluators can assess whether responses are contextually appropriate
and culturally sensitive - factors that may not always be captured accurately by automated measures.

Fine-grained Analysis: While automated metrics offer quantitative scores on specific criteria,
human evaluations provide nuanced qualitative feedback on overall quality including creativity,
emotional resonance which enriches understanding beyond numerical values.

4 .Robust Evaluation: Combining both approaches helps validate results more comprehensively
by cross-verifying findings against multiple benchmarks ensuring reliability
By integrating human judgment with objective measurements from automated tools like ROUGE-L,BERTScore,K-Precision etc., organizations gain a more holistic viewof their language models' capabilities enabling themto make informed decisions about improvements neededfor enhanced performance across various applications