Constructing Benchmarks and Interventions for Combating Hallucinations
  in LLMs
Adi Simhi Jonathan Herzig Idan Szpektor Yonatan Belinkov
Large language models (LLMs) are susceptible to hallucination, which sparked
a widespread effort to detect and prevent them. Recent work attempts to
mitigate hallucinations by intervening in the model's computation during
generation, using different setups and heuristics. Those works lack separation
between different hallucination causes. In this work, we first introduce an
approach for constructing datasets based on the model knowledge for detection
and intervention methods in closed-book and open-book question-answering
settings. We then characterize the effect of different choices for
intervention, such as the intervened components (MLPs, attention block,
residual stream, and specific heads), and how often and how strongly to
intervene. We find that intervention success varies depending on the component,
with some components being detrimental to language modeling capabilities.
Finally, we find that interventions can benefit from pre-hallucination steering
direction instead of post-hallucination. The code is available at
https://github.com/technion-cs-nlp/hallucination-mitigation