Title: Incentive-Aligned LLM Summaries
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: LLM summarization, incentive alignment, truthfulness, retrieval-augmented generation (RAG), peer prediction

Abstract: Large language models (LLMs) are increasingly used in modern search and answer systems to synthesize multiple, sometimes conflicting, texts into a single response, yet current pipelines offer weak incentives for sources to be accurate and are vulnerable to adversarial content. We introduce Truthful Text Summarization (TTS), an incentive-aligned framework that improves factual robustness without ground-truth labels. TTS (i) decomposes a draft synthesis into atomic claims, (ii) elicits each source’s stance on every claim, (iii) scores sources with an adapted multi-task peer-prediction mechanism that rewards informative agreement, and (iv) filters unreliable sources before re-summarizing. We establish formal guarantees that align a source’s incentives with informative honesty, making truthful reporting the utility-maximizing strategy. Experiments show that TTS improves factual accuracy and robustness while preserving fluency, aligning exposure with informative corroboration and disincentivizing manipulation.

TL;DR: We propose an incentive-aligned, claim-level pipeline for LLM search summaries: score sources via multi-task peer prediction, filter low-scorers, re-summarize—rewarding truthful reporting and discouraging manipulation, without ground-truth labels.