On Positional Bias of Faithfulness for Long-form Summarization (2410.23609v1)

Abstract: LLMs often exhibit positional bias in long-context settings, under-attending to information in the middle of inputs. We investigate the presence of this bias in long-form summarization, its impact on faithfulness, and various techniques to mitigate this bias. To consistently evaluate faithfulness, we first compile a benchmark of eight human-annotated long-form summarization datasets and perform a meta-evaluation of faithfulness metrics. We show that LLM-based faithfulness metrics, though effective with full-context inputs, remain sensitive to document order, indicating positional bias. Analyzing LLM-generated summaries across six datasets, we find a "U-shaped" trend in faithfulness, where LLMs faithfully summarize the beginning and end of documents but neglect middle content. Perturbing document order similarly reveals models are less faithful when important documents are placed in the middle of the input. We find that this behavior is partly due to shifting focus with context length: as context increases, summaries become less faithful, but beyond a certain length, faithfulness improves as the model focuses on the end. Finally, we experiment with different generation techniques to reduce positional bias and find that prompting techniques effectively direct model attention to specific positions, whereas more sophisticated approaches offer limited improvements. Our data and code are available in https://github.com/meetdavidwan/longformfact.

• The paper reveals that LLMs display a U-shaped faithfulness pattern by focusing on the beginning and end of documents, leading to neglected middle sections.
• It introduces a comprehensive evaluation framework using eight human-annotated datasets to quantify the impact of positional bias on summary accuracy.
• The study tests mitigation strategies, such as explicit prompts and hierarchical merging, and highlights the need for further research on adaptive attention mechanisms.

Positional Bias of Faithfulness in Long-form Summarization

The paper entitled "On Positional Bias of Faithfulness for Long-form Summarization" examines the perplexing phenomenon of positional bias within LLMs and its implications on faithfulness in long-context scenarios. This investigation is critical, given the widespread reliance on LLMs for generating long-form summaries—a task that demands attention to detail, comprehensive coverage, and factual consistency across lengthy documents.

Positional Bias in Long-form Summarization

LLMs have demonstrated capable of producing high-quality summaries; however, their performance in long-form contexts is often compromised by positional bias. Specifically, LLMs tend to neglect the middle portions of texts, a manifestation known as the "lost-in-the-middle" trend. Through an empirical analysis of various datasets, the researchers identify a "U-shaped" faithfulness pattern where LLMs focus more intently on the initial and final sections of documents, consequently omitting vital information in the middle segments. Such biases can lead to hallucinations, as models hypothesize content rather than relying on overlooked sections—a significant challenge in ensuring faithfulness.

Methodological Approach

The authors establish an evaluation framework to consistently measure faithfulness across long-form summarization tasks. This involves creating a benchmark comprising eight human-annotated datasets and conducting a meta-evaluation of faithfulness metrics. Through experimentation, LLM-based metrics have been found both effective and sensitive to document order alterations, underscoring an inherent positional bias.

A pivotal aspect of this research is the analysis of different models and datasets to ascertain how positional bias impacts summary fidelity. The paper highlights that, as context length increases, models initially stray from faithfulness due to middle-section neglect, but later regain it by concentrating on document ends.

Mitigation Techniques

In exploring techniques to mitigate positional bias, the authors experiment with several generation methodologies. Simple modifications, such as explicit prompts to focus on certain document sections, show promise in guiding LLMs toward neglected parts. Conversely, more sophisticated strategies like hierarchical merging or incremental updates yield limited enhancements and often exacerbate faithfulness issues.

Implications and Future Directions

The exploration of positional biases opens pathways for refining LLMs to enhance their usability in academia, industry, and other domains requiring accurate long-context comprehension. In practical terms, addressing these biases could improve automated reporting systems, educational tools, and any technology relying on precise information extraction. Theoretically, this line of research can propel further studies into the structural and functional adaptations of neural architectures, potentially leading to more balanced attention mechanisms within LLMs.

For future undertakings, the authors suggest an extension of these methodologies to more extensive datasets and models, advocating for deeper investigation into adaptive strategies that can dynamically adjust attention allocation based on input complexity. They also stress the importance of developing more robust metrics that can better accommodate variations introduced by input rearrangement, aiming to bolster LLM faithfulness regardless of document layout.

In summary, the paper presents a thorough and methodical evaluation of how LLMs handle long-form summarization tasks, revealing critical limitations while proposing avenues for progress. As LLMs continue to evolve, addressing positional bias will be crucial in leveraging their full potential for nuanced and extensive text generation applications.