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Language models are increasingly becoming popular in AI-powered scientific IRsystems. This paper evaluates popular scientific language models in handling(i) short-query texts and (ii) textual neighbors. Our experiments showcase theinability to retrieve relevant documents for a short-query text even under themost relaxed conditions. Additionally, we leverage textual neighbors, generatedby small perturbations to the original text, to demonstrate that not allperturbations lead to close neighbors in the embedding space. Further, anexhaustive categorization yields several classes of orthographically andsemantically related, partially related, and completely unrelated neighbors.Retrieval performance turns out to be more influenced by the surface formrather than the semantics of the text.

The Inefficiency of Language Models in Scholarly Retrieval: An Experimental Walk-through