Incentive-Aligned Multi-Source LLM Summaries
This addresses the issue of adversarial content in search and answer systems for users relying on synthesized information, representing a novel method rather than an incremental improvement.
The paper tackles the problem of unreliable sources in multi-source LLM summarization by introducing an incentive-aligned framework that improves factual accuracy and robustness without ground-truth labels, achieving formal guarantees for truthful reporting as the optimal strategy.
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.