让大模型总结更真实：通过激励机制筛选可信来源
Incentive-Aligned Multi-Source LLM Summaries
- 将摘要拆成原子事实，逐条评估各来源立场
- 用同伴预测机制评分，真实一致的来源得分更高
- 无需真实标签也能提升准确性，适合问答系统使用
大型语言模型在现代搜索与问答系统中被广泛用于整合多源文本，但现有流程对信息源缺乏准确激励，易受对抗性内容影响。本文提出真相文本摘要（TTS），一种激励对齐框架，在无真实标签情况下提升事实稳健性。TTS将草稿分解为原子命题，获取各来源对每个命题的立场，采用改进的多任务同伴预测机制评分，奖励具有信息量的一致性，再过滤不可靠来源并重新生成摘要。理论证明该机制使真实报告成为最优策略。实验表明，TTS显著提升事实准确性和鲁棒性，同时保持流畅性，实现曝光与信息支持的对齐，并抑制操纵行为。
原文摘要 · Abstract (English)
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.
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