Argument Quality Assessment Challenges and Opportunities with Large Language Models

Core Concepts

Instruction-following large language models offer a promising solution to the challenges of diverse quality notions and subjectivity in argument quality assessment.

Abstract

Introduction: The importance of assessing argument quality in various applications.
Survey of Recent Research: Three main research directions identified: Conceptual Notions, Influence Factors, Computational Models.
LLMs for Argument Quality: Advantages of instruction-following LLMs for overcoming limitations in traditional supervised learning.
Blueprint for Instruction Fine-Tuning: Proposed systematic approach to instruct LLMs for argument quality assessment.
Opportunities for the Real World: Applications in debating technologies, argument search, discussion moderation, and writing support.
Ethics Statement: Addressing limitations and ethical concerns related to using LLMs for argument quality assessment.

Stats

"The computational treatment of arguments on controversial issues has been subject to extensive NLP research."
"In this position paper, we start from a brief survey of argument quality research."
"We argue that instruction-following large language models (LLMs) have the potential to overcome many limitations."

Quotes

"In some sense, the question about the quality of an argument is the ‘ultimate’ one for argumentation mining." - Stede and Schneider (2018)
"We argue that instruction-following large language models (LLMs) have the potential to overcome many aspects of the two challenges." - Content

Key Insights Distilled From

Argument Quality Assessment in the Age of Instruction-Following Large Language Models

by Henning Wach... at arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.16084.pdf

Deeper Inquiries

How can instruction-following LLMs be effectively trained to assess diverse notions of argument quality?

Instruction-following Large Language Models (LLMs) can be effectively trained to assess diverse notions of argument quality by following a systematic approach. Here are the key steps:

Seed Set of Instructions: Start with a seed set of argumentation-specific instructions covering various concepts such as arguing goals, different quality notions, audience specifics, ethical considerations, and examples for respective assessments.

Fine-Tuning Process: Apply techniques like reinforcement learning using human feedback or self-generated instructions for fine-tuning the LLM on these specific instructions related to argument quality assessment.

Alignment through Prompt Design: Align the behavior of the instruction fine-tuned LLM on new unseen tasks by designing prompts systematically. This could involve soft prompting or sociodemographic prompting to emulate social profiles of debaters and audiences.

Fact-Checking Mechanisms: Incorporate fact-checking mechanisms in cases where factual accuracy is crucial for assessing arguments accurately and avoiding misinformation.

Evaluation Criteria: Develop evaluation criteria that consider both absolute assessment (e.g., logical soundness) and relative assessment (e.g., comparison with other arguments) to ensure comprehensive evaluation of diverse notions of argument quality.

How can ethical concerns regarding biases and privacy be addressed when implementing LLMs for argument quality assessment?

Addressing ethical concerns related to biases and privacy when implementing LLMs for argument quality assessment is crucial. Here's how these concerns can be mitigated:

Bias Detection and Mitigation: Implement bias detection algorithms within the LLM architecture to identify potential biases in training data or generated outputs. Take corrective actions such as retraining models with balanced datasets or adjusting decision-making processes accordingly.

Privacy Protection Measures: Ensure robust data anonymization techniques are applied during model training to protect user privacy rights while still maintaining effective performance levels.

Ethical Guidelines Compliance: Adhere strictly to established ethical guidelines governing AI applications, especially in sensitive areas like opinion formation or educational support where personal beliefs may be influenced by automated systems.

Transparency & Accountability: Maintain transparency about how LLMs make decisions regarding argument quality assessments, allowing users insight into the process while holding developers accountable for any unintended consequences arising from biased outputs.

What are the implications of using LLMs for real-world applications like debating technologies and discussion moderation?

The implications of utilizing Instruction-Following Large Language Models (LLMs) in real-world applications such as debating technologies and discussion moderation are significant:

Enhanced Quality Assessment: By leveraging advanced language processing capabilities, LLMs can provide more accurate evaluations of arguments based on diverse criteria like persuasiveness, relevance, clarity, etc., improving overall discourse standards.

2 .Efficient Moderation: In discussion moderation settings, well-trained LLMs can assist moderators in identifying inappropriate content quickly by detecting violations against community guidelines or low-quality contributions.
3 .Personalized Education: For educational purposes like debate coaching or writing assistance platforms, personalized feedback provided by instructed-LMMs helps learners improve their reasoning skills tailored towards individual needs.
4 .Debate Enrichment: Debating technologies benefit from generating compelling arguments that resonate with varied audiences due to nuanced understanding enabled through instruction fine-tuning.
5 .Improved Search Algorithms: Argument search engines powered by instructed-LMMs offer better ranking results based on nuanced factors beyond keyword matching alone leading users towards high-quality information sources efficiently
These implications underscore how integrating Instruction-Following Large Language Models into real-world applications enhances functionality across various domains while also raising important considerations around ethics and bias mitigation strategies that need careful attention during implementation efforts