mlsanityData to 7 Oct 2026

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

arXiv 2401.01989 ↗First posted 3 Jan 2024cs.CL cs.AIcited by 28

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Abstract (arXiv)

We characterize and study zero-shot abstractive summarization in Large Language Models (LLMs) by measuring position bias, which we propose as a general formulation of the more restrictive lead bias phenomenon studied previously in the literature. Position bias captures the tendency of a model unfairly prioritizing information from certain parts of the input text over others, leading to undesirable behavior. Through numerous experiments on four diverse real-world datasets, we study position bias in multiple LLM models such as GPT 3.5-Turbo, Llama-2, and Dolly-v2, as well as state-of-the-art pretrained encoder-decoder abstractive summarization models such as Pegasus and BART. Our findings lead to novel insights and discussion on performance and position bias of models for zero-shot summarization tasks.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). Builds on 17 of its 35 references that are in the map. First posted on arXiv 3 Jan 2024.

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17 of 35 references.

References · 17

  1. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  2. 2022Scaling Instruction-Finetuned Language Modelscited by 2,997
  3. 2018Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarizationcited by 1,308
  4. 2017Get To The Point: Summarization with Pointer-Generator Networkscited by 1,281
  5. 2019PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarizationcited by 1,100
  6. 2019Text Summarization with Pretrained Encoderscited by 553
  7. 2023Benchmarking Large Language Models for News Summarizationcited by 444
  8. 2022News Summarization and Evaluation in the Era of GPT-3cited by 324
  9. 2023How is ChatGPT's behavior changing over time?cited by 265
  10. 2023Better Zero-Shot Reasoning with Role-Play Promptingcited by 220
  11. 2018Abstractive Summarization of Reddit Posts with Multi-level Memory Networkscited by 102
  12. 2022Evaluating the Factual Consistency of Large Language Models Through News Summarizationcited by 90
5 more
  1. 2023ChatGPT: A Meta-Analysis after 2.5 Monthscited by 50
  2. 2016Revisiting Summarization Evaluation for Scientific Articlescited by 31
  3. 2019Countering the Effects of Lead Bias in News Summarization via Multi-Stage Training and Auxiliary Lossescited by 22
  4. 2022Robust Fair Clustering: A Novel Fairness Attack and Defense Frameworkcited by 13
  5. 2021Demoting the Lead Bias in News Summarization via Alternating Adversarial Learningcited by 10

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Citing papers per year: 2024: 10; 2025: 12; 2026: 6.

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  1. 2024A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Modelscited by 52
  2. 2024Circuit Complexity Bounds for RoPE-based Transformer Architecturecited by 26
  3. 2024Multi-Layer Transformers Gradient Can be Approximated in Almost Linear Timecited by 22
  4. 2024Improving Faithfulness of Large Language Models in Summarization via Sliding Generation and Self-Consistencycited by 14
  5. 2024A Survey of Deep Long-Tail Classification Advancementscited by 11
  6. 2024Assessing LLMs for Zero-shot Abstractive Summarization Through the Lens of Relevance Paraphrasingcited by 11
  7. 2024Fairness Definitions in Language Models Explainedcited by 10
  8. 2025StrucSum: Graph-Structured Reasoning for Long Document Extractive Summarization with LLMscited by 7
  9. 2025Watching the AI Watchdogs: A Fairness and Robustness Analysis of AI Safety Moderation Classifierscited by 5
  10. 2024What Affects the Stability of Tool Learning? An Empirical Study on the Robustness of Tool Learning Frameworkscited by 4
  11. 2024Disentangling Likes and Dislikes in Personalized Generative Explainable Recommendationcited by 3
  12. 2024Summarization of Opinionated Political Documents with Varied Perspectivescited by 3
16 more
  1. 2025Can Large Language Models be Effective Online Opinion Miners?cited by 3
  2. 2025Re-ranking Using Large Language Models for Mitigating Exposure to Harmful Content on Social Media Platformscited by 2
  3. 2025RoToR: Towards More Reliable Responses for Order-Invariant Inputscited by 2
  4. 2025DiffSampling: Enhancing Diversity and Accuracy in Neural Text Generationcited by 2
  5. 2025From Anger to Joy: How Nationality Personas Shape Emotion Attribution in Large Language Modelscited by 2
  6. 2025Reranking-based Generation for Unbiased Perspective Summarizationcited by 2
  7. 2025"Whose Side Are You On?" Estimating Ideology of Political and News Content Using Large Language Models and Few-shot Demonstration Selectioncited by 1
  8. 2025Bridging the Culture Gap: A Framework for LLM-Driven Socio-Cultural Localization of Math Word Problems in Low-Resource Languagescited by 1
  9. 2025Input Order Shapes LLM Semantic Alignment in Multi-Document Summarizationcited by 1
  10. 2026Token-Level Diagnosis of Sycophancy in LLMs with Attribution-Guided Steeringcited by 1
  11. 2025From Ground Trust to Truth: Disparities in Offensive Language Judgments on Contemporary Korean Political Discoursecited by 0
  12. 2026DIAL-SUMMER: A Structured Evaluation Framework of Hierarchical Errors in Dialogue Summariescited by 0
  13. 2026Enhancing Factuality through Consensus and Consistency in Summarization Using Minimum Bayes Risk Decodingcited by 0
  14. 2026Dialogue Summarization with Emotion Dynamics Using Topic- and Participant-Centric Decompositioncited by 0
  15. 2026Decoupling Generation and Selection for Budget-Constrained Faithful Summarizationcited by 0
  16. 2026When Less Is Enough: Context Selection and Prompting Strategies for Bengali News Headline Generationcited by 0