Analyzing Position Bias in Zero-Shot Abstractive Summarization with Large Language Models

Khái niệm cốt lõi

Position bias in large language models affects the quality of zero-shot abstractive summarization.

Tóm tắt

The study examines position bias in Large Language Models (LLMs) for zero-shot abstractive summarization. Position bias is defined as the unfair prioritization of information from certain parts of the input text, leading to undesirable behavior. Various LLM models and pretrained encoder-decoder models are analyzed on real-world datasets like CNN/DM, Reddit TL;DR, News Summary, and XSum. The research provides insights into model performance and position bias, highlighting the challenges and opportunities in leveraging LLMs for effective abstractive summarization.

Thống kê

GPT 3.5-T is large (175B params)
Llama2-13B-chat is mid-size (13B params)
Dolly-v2-7B is small (7B params)

Trích dẫn

"Lead bias can then be understood as a specific case of position bias."
"Our findings lead to novel insights on performance and position bias of models for zero-shot summarization tasks."

Thông tin chi tiết chính được chắt lọc từ

Revisiting Zero-Shot Abstractive Summarization in the Era of Large Language Models from the Perspective of Position Bias

by Anshuman Chh... lúc arxiv.org 03-18-2024

https://arxiv.org/pdf/2401.01989.pdf

Yêu cầu sâu hơn

How can position bias impact the credibility of automated summaries?

Position bias in automated summarization models can significantly impact the credibility of the generated summaries. When a model exhibits position bias, it tends to prioritize information from specific sections of the input text over others, potentially overlooking crucial details present in other parts of the article. This biased behavior can lead to inaccuracies and distortions in the summary output.
The credibility of an automated summary relies on its ability to accurately capture and represent essential information from the source text. Position bias skews this representation by disproportionately emphasizing certain sections while neglecting others. As a result, the summary may lack completeness, coherence, and accuracy, diminishing its overall trustworthiness.
In scenarios where position bias is prevalent, users relying on these automated summaries may receive distorted or incomplete information that does not reflect the original content effectively. This could lead to misunderstandings, misinterpretations, or even misinformation if critical details are omitted or misrepresented due to biased summarization processes.
Addressing and mitigating position bias is crucial for ensuring that automated summaries maintain their credibility by providing accurate and comprehensive representations of the source material without undue influence from specific sections.

How can using multiple article sentences to map summary sentences affect measuring position bias?

When utilizing multiple article sentences to map summary sentences for measuring position bias in automated summarization models, several implications arise:

Enhanced Contextual Understanding: By considering multiple article sentences instead of just one for each summary sentence mapping, there is a potential for a more comprehensive understanding of how different parts of an article contribute to generating each aspect of a summary sentence.

Increased Granularity: Using multiple matches allows for capturing nuances in how various segments within an article influence different elements within a generated summary. This finer granularity provides insights into which specific content contributes most significantly towards particular aspects of a summarized output.

Improved Accuracy: Incorporating top matches when mapping enhances precision by accounting for alternative sources contributing relevant information used in generating each part of a summarized text segment.

Complexity Consideration: While using multiple matches offers benefits such as improved context comprehension and increased accuracy in analyzing positional biases across articles and their corresponding summaries; however it also introduces complexity regarding data processing requirements and computational resources needed for analysis.

How can prompt engineering methods like role-playing affect summarization position bias?

Prompt engineering methods like role-playing have shown promise in influencing summarization performance metrics including addressing issues related to positional biases:

Enhanced Prompt Specificity: Role-playing enables fine-tuning prompts based on diverse scenarios or perspectives leading LLMs towards producing more contextually relevant outputs tailored according to specified criteria thereby reducing inherent biases associated with generic prompts.

Bias Mitigation through Diverse Inputs: By incorporating varied roles or viewpoints during training via role-play prompting techniques; LLMs are exposed to diverse linguistic patterns enhancing adaptability resulting in reduced tendencies towards favoring specific segments thus minimizing positional biases.

3 .Performance Optimization & Generalizability: Role-played prompts facilitate optimizing model performance across datasets promoting generalizability while simultaneously aiding robustness against common pitfalls such as lead-bias phenomenon often observed during abstractive summarizations tasks.
4 .Interpretation & Explainability: Leveraging role-played prompts not only improves task-specific performance but also aids interpretability allowing practitioners insight into underlying mechanisms driving model decisions hence facilitating better control over potential biases including those related specifically with positional influences within generated outputs