Authors: Yuxia Wang, Minghan Wang, Muhammad Arslan Manzoor, Georgi Georgiev, Rocktim Jyoti Das, Preslav Nakov
Published on: February 04, 2024
Impact Score: 8.45
Arxiv code: Arxiv:2402.0242
Summary
- What is new: This survey critically analyzes the latest efforts in improving the factuality of Large Language Models (LLMs) tuned for chat, identifying new challenges and obstacles to factuality evaluation.
- Why this is important: LLM responses, despite their integration into daily life, often contain factual inaccuracies, limiting their practical use.
- What the research proposes: The paper proposes a comprehensive analysis of existing solutions and outlines potential strategies for enhancing accuracy.
- Results: It offers insight into the primary causes of inaccuracies and suggests a direction for future research to mitigate these issues.
Technical Details
Technological frameworks used: Instruction-tuning for chat LLMs
Models used: Survey of models targeting improved factuality
Data used: Analysis of previous literature and datasets assessing LLM factuality
Potential Impact
Search engines, digital assistants, educational platforms, and content creators could be significantly impacted by advancements in LLM factuality.
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