---
title: "Whose Facts Win? LLM Source Preferences under Knowledge"
description: "The authors propose a fine-tuning method, combined with credibility prompting, that sharply reduces… 92 references, cited by 1 paper here."
authors: ["Jakob Schuster", "Vagrant Gautam", "Katja Markert"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.1357/"
doi: "10.18653/v1/2026.acl-long.1357"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/schuster-2026-whose-facts-win-llm-source/"
---
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# Whose Facts Win? LLM Source Preferences under Knowledge Conflicts

**Authors:** Jakob Schuster, [Vagrant Gautam](https://md.research.elamur.ai/free/en/authors/vagrant-gautam/) and [Katja Markert](https://md.research.elamur.ai/free/en/authors/katja-markert/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.1357/) · [PDF](https://aclanthology.org/2026.acl-long.1357.pdf) · [DOI 10.18653/v1/2026.acl-long.1357](https://doi.org/10.18653/v1/2026.acl-long.1357) · [OpenAlex](https://openalex.org/W7166813944)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · 1 in our library, of the 149,439 papers whose bibliographies we read

## Summary
LLMs used in retrieval-augmented generation often receive passages that contradict each other. Earlier work on these inter-context knowledge conflicts did not ask whether the attributed source affects which version a model believes, and this paper addresses that gap. The authors built a controlled English testbed. They altered a single attribute of fictional NeoQA entities to create equally plausible conflicting pairs, presented as two Markdown tables. Each table was labelled with an invented source (newspaper, government body, social media account or private person) or with no source. Thirteen open-weight instruction-tuned models answered forced-choice multiple-choice questions, and shifts in answer probabilities revealed their source preferences. The models consistently trusted institutional sources, government and newspapers, more than individuals and social media users. However, repeating a claim from a weaker source could reverse this ordering, and prompting alone did not fix the problem. The authors propose a fine-tuning method, combined with credibility prompting, that sharply reduces this repetition bias while keeping most original preferences. This matters for making RAG robust to disinformation.

## Key points
- **Dataset:** 7,440 conflict pairs built from 373 NeoQA entities of seven types, covering 1,903 counterfactually perturbed attribute values. QWEN 2.5-72B helped generate alternatives, and one author manually checked them.
- **Synthetic sources:** four types. Newspaper names come from 59 templates mined from Media Bias/Fact Check, and government agency names were generated with templates. Social media handles mimic Reddit's username style, and person names are drawn from US Census/SSA data. Names with English Wikipedia pages were removed.
- **Models:** 13 models from four families: QWEN 2.5 (7B–72B), OLMO-2 (7B–32B), LLAMA-3.1/3.2 (3B–70B) and GEMMA-3 (4B–27B).
- **Main finding:** the models follow a stable credibility hierarchy, with government ahead of newspapers, which are ahead of persons and social media. Repeating low-credibility information can flip this order.
- **Mitigation:** the fine-tuning method lowers repetition bias by up to 79.2% and retains at least 72.5% of the original preferences, even for source types not seen in training. Data and code are released on GitHub (JaSchuste/llm-source-preference).

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Jakob Schuster, Vagrant Gautam and Katja Markert. 2026. Whose Facts Win? LLM Source Preferences under Knowledge Conflicts. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{schuster2026whose,
  title = {{Whose Facts Win? LLM Source Preferences under Knowledge Conflicts}},
  author = {Jakob Schuster, Vagrant Gautam and Katja Markert},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1357},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1357}
}
```

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*Updated 31 October 2026*