# Rational Synthesizers or Heuristic Followers? Analyzing LLMs in RAG-based Question-Answering

arXiv: https://arxiv.org/abs/2601.06189 · HTML: https://mlsanity.com/paper/2601.06189/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation.
- Builds on 12 of its 19 references that are in the map.
- First posted on arXiv 8 Jan 2026.

## Placed in

- Retrieval-augmented generation / [RAG robustness and adaptive retrieval](https://mlsanity.com/rag/rag-robustness-and-adaptive-retrieval/)

## Builds on (most cited first, 12 of 12)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [Efficient Streaming Language Models with Attention Sinks](https://mlsanity.com/paper/2309.17453/) (2023)
- [REALM: Retrieval-Augmented Language Model Pre-Training](https://mlsanity.com/paper/2002.08909/) (2020)
- [Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?](https://mlsanity.com/paper/2202.12837/) (2022)
- [Making Retrieval-Augmented Language Models Robust to Irrelevant Context](https://mlsanity.com/paper/2310.01558/) (2023)
- [Entity-Based Knowledge Conflicts in Question Answering](https://mlsanity.com/paper/2109.05052/) (2021)
- [Knowledge Conflicts for LLMs: A Survey](https://mlsanity.com/paper/2403.08319/) (2024)
- [Certifiably Robust RAG against Retrieval Corruption](https://mlsanity.com/paper/2405.15556/) (2024)
- [Enhancing Noise Robustness of Retrieval-Augmented Language Models with Adaptive Adversarial Training](https://mlsanity.com/paper/2405.20978/) (2024)
- [What Evidence Do Language Models Find Convincing?](https://mlsanity.com/paper/2402.11782/) (2024)
- [Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain](https://mlsanity.com/paper/2509.03787/) (2025)

## Built on by (most cited first, 5 of 5)

- [Adversarial Feeds Steer LLM Agent Decisions Against Their Defaults](https://mlsanity.com/paper/2606.00914/) (2026)
- [Record Grouping Controls Evidence Weight in Language Models](https://mlsanity.com/paper/2609.08698/) (2026)
- [Copies or Sources? Measuring How LLM Aggregators Count Restated Evidence in Multi-Agent Systems](https://mlsanity.com/paper/2610.06192/) (2026)
- [Provenance-Aware Belief Updating in Human-AI Feedback Loops](https://mlsanity.com/paper/2610.13008/) (2026)
- [Where Do LLM Ensemble Gains Come From? Decomposing Candidate Value, Diversity, and Aggregation](https://mlsanity.com/paper/2610.18786/) (2026)

## Abstract (arXiv)

Retrieval-Augmented Generation (RAG) is the prevailing paradigm for grounding Large Language Models (LLMs), yet the mechanisms governing how models integrate groups of conflicting retrieved evidence remain opaque. Does an LLM answer a certain way because the evidence is factually strong, because of a prior belief, or merely because it is repeated frequently? To answer this, we introduce GroupQA, a curated dataset of 1,635 controversial questions paired with 15,058 diversely-sourced evidence documents, annotated for stance and qualitative strength. Through controlled experiments, we characterize group-level evidence aggregation dynamics: Paraphrasing an argument can be more persuasive than providing distinct independent support; Models favor evidence presented first rather than last, and Larger models are increasingly resistant to adapt to presented evidence. Additionally, we find that LLM explanations to group-based answers are unfaithful. Together, we show that LLMs behave consistently as vulnerable heuristic followers, with direct implications for improving RAG system design.