LLM-Knowledge-Conflict
[ICLR'24 Spotlight] Revealing the Behavior of Large Language Models in Knowledge Conflicts
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Decision brief
LLM-Knowledge-Conflict provides specific datasets and tools to understand how large language models handle knowledge conflicts by using parametric memory techniques.
Good fit when
- When you want to evaluate the robustness of a large language model's responses in scenarios where conflicting information is available.
- To analyze and improve the consistency and accuracy of responses from multiple pre-trained LLMs like ChatGPT, GPT-4, etc., by using parametric memory aligned evidence.
Avoid when
- If your objective is to train new large language models rather than evaluate existing ones under specific scenarios.
- When you require a general-purpose natural language processing toolkit that includes tasks beyond the scope of knowledge conflict evaluation.
Observed Jul 12, 2026 · Source: enrich:decision_facts
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Maintenance and security
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Install
pip install LLM-Knowledge-Conflict PyPISimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
This project provides datasets and tools aimed at understanding how large language models handle knowledge conflict scenarios through a suite of parametric memory techniques.
Capability facts
- Languages
- python
Source: github.language · Aug 1, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 1, 2026)
choose dataset "ConflictQA-popQA-[PLACEHOLDER]", and the [PLACEHOLDER] is in ["chatgpt","gpt4","palm2","llama2-7b","llama2-70b","qwen7b","vicuna7b","vicuna33b"].Source link
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README
[ICLR'24 Spotlight] LLM-knowledge-conflict
ConflictQA
We provide the conflictQA based on different large language models, which utilizes large language models guided parametric memory.
The data is available at conflictQA foloder. This folder contains the data for both POPQA and STRATEGYQA
{"question": "What is George Rankin's occupation?", "popularity": 142, "ground_truth": ["politician", "political leader", "political figure", "polit.", "pol"], "memory_answer": "George Rankin's occupation is a professional photographer.", "parametric_memory": "As a professional photographer, George Rankin...", "counter_answer": "George Rankin's occupation is political figure.", "counter_memory": "George Rankin has been actively involved in politics for over a decade...", "parametric_memory_aligned_evidence": "George Rankin has a website showcasing his photography portfolio...", "counter_memory_aligned_evidence": "George Rankin Major General George James Rankin..."}
- "question": The question in natural language
- "popularity": The monthly page views on Wikipedia for the given question
- "ground_truth": The factual answer to the question, which may include multiple possible answers
- "memory_answer": The answer provided by the LLM to the question
- "parametric_memory": The supportive evidence from LLM's parametric memory for the answer
- "counter_answer": The answer contradicting the "memory_answer"
- "counter_memory": The generation-based evidence supporting the counter_answer
- "parametric_memory_aligned_evidence": Additional evidence supporting the "memory_answer", which could be generated or derived from Wikipedia/human annotation
- "counter_memory_aligned_evidence": Additional evidence supporting the "counter_answer", either generated or sourced from Wikipedia/human annotation
We also release our dataset at: Huggingface datasets: https://huggingface.co/datasets/osunlp/ConflictQA (more details can be found on the dataset page)
#loading dataset
from datasets import load_dataset
# you can choose dataset "ConflictQA-popQA-[PLACEHOLDER]", and the [PLACEHOLDER] is in ["chatgpt","gpt4","palm2","llama2-7b","llama2-70b","qwen7b","vicuna7b","vicuna33b"].
dataset = load_dataset("osunlp/ConflictQA",'ConflictQA-popQA-chatgpt')
Code is available in code foloder.
Citation
If our paper or related resources prove valuable to your research, we kindly ask for citation. Please feel free to contact us with any inquiries.
@inproceedings{
xie2024knowledgeconflict,
title={Adaptive Chameleon or Stubborn Sloth: Revealing the Behavior of Large Language Models in Knowledge Conflicts},
author={Jian Xie and Kai Zhang and Jiangjie Chen and Renze Lou and Yu Su},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=auKAUJZMO6}
}
For agents
This page has a .md twin and JSON over the API.