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Computer Science > Information Retrieval

arXiv:2203.15364 (cs)
[Submitted on 29 Mar 2022]

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

Authors:Shruti Singh, Mayank Singh
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Abstract:Language models are increasingly becoming popular in AI-powered scientific IR systems. This paper evaluates popular scientific language models in handling (i) short-query texts and (ii) textual neighbors. Our experiments showcase the inability to retrieve relevant documents for a short-query text even under the most relaxed conditions. Additionally, we leverage textual neighbors, generated by small perturbations to the original text, to demonstrate that not all perturbations lead to close neighbors in the embedding space. Further, an exhaustive categorization yields several classes of orthographically and semantically related, partially related, and completely unrelated neighbors. Retrieval performance turns out to be more influenced by the surface form rather than the semantics of the text.
Comments: 21 pages. To appear in Findings of ACL 2022
Subjects: Information Retrieval (cs.IR); Computation and Language (cs.CL)
Cite as: arXiv:2203.15364 [cs.IR]
  (or arXiv:2203.15364v1 [cs.IR] for this version)
  https://doi.org/10.48550/arXiv.2203.15364

Submission history

From: Shruti Singh [view email]
[v1] Tue, 29 Mar 2022 09:01:26 UTC (21,467 KB)
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