Revisiting the Knowledge Injection Frameworks
Peng Fu Yiming Zhang Haobo Wang Weikang Qiu Junbo Zhao
In recent years, large language models (LLMs), such as GPTs, have attained
great impact worldwide. However, how to adapt these LLMs to better suit the
vertical domain-specific tasks by utilizing external knowledge remains not
completely solved. Indeed, there have emerged a few works on this line where
most of them rely on an alignment heuristic that is built to inject the
corresponding knowledge tuple into the associated text sample.
  However, despite the promise, we identify a pivotal problem in this work
ubiquitously. Simply put, we find that injecting unaligned (i.e., random)
knowledge tuple into the LLMs achieves comparable (and sometimes better)
results than the aligned knowledge being injected. We therefore take a thorough
investigation of this frustrating finding on a variety of related prior work
and further provide a chain of potential interpretations for the phenomenon.
Based on all that, we offer a simple remediated technique. Briefly, the core of
this technique is rooted in an ideological emphasis on the pruning and
purification of the external knowledge base to be injected into LLMs. At last,
we show that by integrating this technique into most (if not all) knowledge
injection frameworks and recent LLMs, it manages to overcome the aforementioned
sanity problem and further pushes the boundary of the performance of the
domain-adaptive LLMs.