LLMs as Factual Reasoners: Insights from Existing Benchmarks and Beyond
This addresses the problem of misinformation propagation by improving factual inconsistency detection methods for LLM users, though it is incremental as it builds on existing benchmarks and methods.
The study found that while some large language models (LLMs) perform competitively on existing factual inconsistency detection benchmarks, they fail on more complex tasks and expose issues with these benchmarks, leading to the creation of a new 10-domain benchmark called SummEdits that is 20 times more cost-effective per sample and shows LLMs struggling, with GPT-4 still 8% below human performance.
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.