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.- Anthology ID:
- 2022.findings-acl.249
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2022
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Editors:
- Smaranda Muresan, Preslav Nakov, Aline Villavicencio
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3153–3173
- Language:
- URL:
- https://aclanthology.org/2022.findings-acl.249
- DOI:
- 10.18653/v1/2022.findings-acl.249
- Cite (ACL):
- Shruti Singh and Mayank Singh. 2022. The Inefficiency of Language Models in Scholarly Retrieval: An Experimental Walk-through. In Findings of the Association for Computational Linguistics: ACL 2022, pages 3153–3173, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- The Inefficiency of Language Models in Scholarly Retrieval: An Experimental Walk-through (Singh & Singh, Findings 2022)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2022.findings-acl.249.pdf
- Code
- shruti-singh/scilm_exp