TruthEval: A Dataset to Evaluate LLM Truthfulness and Reliability
This addresses the need for better evaluation tools for LLMs, though it is incremental as it builds on existing benchmarking efforts.
The authors tackled the problem of insufficient benchmarks for evaluating LLM truthfulness and reliability by creating TruthEval, a curated dataset of challenging statements with known truth values, and found instances of LLMs failing in simple tasks.
Large Language Model (LLM) evaluation is currently one of the most important areas of research, with existing benchmarks proving to be insufficient and not completely representative of LLMs' various capabilities. We present a curated collection of challenging statements on sensitive topics for LLM benchmarking called TruthEval. These statements were curated by hand and contain known truth values. The categories were chosen to distinguish LLMs' abilities from their stochastic nature. We perform some initial analyses using this dataset and find several instances of LLMs failing in simple tasks showing their inability to understand simple questions.