anchor
Findings
- IC-1133LLMs are highly receptive to coherent counter-memory as sole evidence, contradicting prior findings of stubbornness with entity-substitution counter-memory [eval]
- IC-1134LLMs show strong confirmation bias in multi-source settings, preferring evidence consistent with parametric memory, with stronger bias for popular entities [eval]
- IC-1135LLMs show order sensitivity to evidence position in context, with PaLM2 and LLaMA2-7B showing memorization ratio variations exceeding 30% [eval]
- IC-1170GPT-3.5, Llama2, PaLM2, and GPT-4 are susceptible to a CoT-prompting backdoor attack (BadChain) on complex reasoning tasks, with stronger reasoning models showing higher attack success rates [eval]
- IC-1264LLMs are overconfident when verbalizing confidence, with values concentrated in 80–100% and multiples of 5, yielding high ECE across all five tested models [eval]
- IC-1265Calibration and failure prediction improve as model capability scales from GPT-3 to GPT-4, but remain far from ideal [eval]
- IC-1266For GPT-3, white-box token-probability methods outperform black-box verbalized confidence in uncertainty estimation, but the gap is narrow (0.522–0.605 AUROC) and both remain near random [eval]
- IC-1499Self-rationalization quality and task accuracy scale with model size across GPT-3, FLAN-T5, and LLaMA on five QA datasets [eval]
- IC-327Llama-3-8B-Instruct, Mistral-7B-Instruct-v0.3, and several other LLMs produce well-calibrated verbal confidence estimates on classification tasks [eval]
- IC-328Llama-3-8B-Instruct and Mistral-7B-Instruct-v0.3 are susceptible to confidence-elicitation-guided word substitution attacks, with CEAttack outperforming existing hard-label black-box methods [eval]
- IC-329GPT-4o is more robust to confidence-elicitation-guided word substitution attacks than open-source LLMs, with lower attack success rates and better confidence calibration [eval]