LLMs as Factual Reasoners: Insights from Existing Benchmarks and Beyond¶
Arxiv Link - 2023-05-23 21:50:06
Abstract¶
With the recent appearance of LLMs in practical settings, having methods that can effectively detect factual inconsistencies is crucial to reduce the propagation of misinformation and improve trust in model outputs. When testing on existing factual consistency benchmarks, we find that a few large language models (LLMs) perform competitively on classification benchmarks for factual inconsistency detection compared to traditional non-LLM methods. However, a closer analysis reveals that most LLMs fail on more complex formulations of the task and exposes issues with existing evaluation benchmarks, affecting evaluation precision. To address this, we propose a new protocol for inconsistency detection benchmark creation and implement it in a 10-domain benchmark called SummEdits. This new benchmark is 20 times more cost-effective per sample than previous benchmarks and highly reproducible, as we estimate inter-annotator agreement at about 0.9. Most LLMs struggle on SummEdits, with performance close to random chance. The best-performing model, GPT-4, is still 8\% below estimated human performance, highlighting the gaps in LLMs' ability to reason about facts and detect inconsistencies when they occur.
Socials¶
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| 🚀 Exciting advancements in the field of Language Models! 🤖📚 Detecting factual inconsistencies in AI-generated content is vital for combatting misinformation and enhancing trust in AI models. Recent research has shown that while some Large Language Models (LLMs) excel in identifying factual inconsistencies in standard benchmarks, they struggle with more complex tasks, showcasing the need for improved evaluation methods. Discover more about the cutting-edge research on factual inconsistency detection and the proposed SummEdits benchmark in the full article here: Check out the research paper! #AI #NLP #LLMs #Research #FactualInconsistencyDetection #TechInnovation ✨ |
🚀 Just in: New research on detecting factual inconsistencies in LLMs! While some LLMs perform well on existing benchmarks, a new 10-domain benchmark called SummEdits reveals their limitations. Explore the study at: http://arxiv.org/abs/2305.14540v1 #AI #NLP #LLMs #TechResearch 🤖🔍 |