Will AI be overconfident about academic research findings when reliant on abstracts? (v1)
Fuente:
arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866914605447512064 |
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| author | Thelwall, Mike |
| author_facet | Thelwall, Mike |
| contents | Large Language Models (LLMs) like ChatGPT, DeepSeek and Gemini seem to be increasingly used for knowledge discovery, information retrieval, and knowledge summaries, including for academic topics. This can result in users being misled, such as due to hallucinations. These problems may be exacerbated for academic knowledge if LLMs base their answers on journal article abstracts when they lack full text access. To test whether the information content of abstracts can be misleading, full text articles were submitted to the GPT-OSS 120B, an LLM from OpenAI, asking it to assess separately the strength the claims for the main result in the abstract, discussion, and conclusion. Outside the social sciences and humanities, claims tended to be stronger in the abstract and conclusions than the discussion, suggesting that relying on the strength of claims in abstracts would be misleading. Thus, if LLMs ingest abstracts but not full texts, there is a risk that they will be overconfident about the findings and pass it on to users in response to relevant prompts. This is another reason to be cautious about using LLMs for academic-related knowledge discovery and summaries. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_27392 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Will AI be overconfident about academic research findings when reliant on abstracts? (v1) Thelwall, Mike Information Retrieval Digital Libraries Large Language Models (LLMs) like ChatGPT, DeepSeek and Gemini seem to be increasingly used for knowledge discovery, information retrieval, and knowledge summaries, including for academic topics. This can result in users being misled, such as due to hallucinations. These problems may be exacerbated for academic knowledge if LLMs base their answers on journal article abstracts when they lack full text access. To test whether the information content of abstracts can be misleading, full text articles were submitted to the GPT-OSS 120B, an LLM from OpenAI, asking it to assess separately the strength the claims for the main result in the abstract, discussion, and conclusion. Outside the social sciences and humanities, claims tended to be stronger in the abstract and conclusions than the discussion, suggesting that relying on the strength of claims in abstracts would be misleading. Thus, if LLMs ingest abstracts but not full texts, there is a risk that they will be overconfident about the findings and pass it on to users in response to relevant prompts. This is another reason to be cautious about using LLMs for academic-related knowledge discovery and summaries. |
| title | Will AI be overconfident about academic research findings when reliant on abstracts? (v1) |
| topic | Information Retrieval Digital Libraries |
| url | https://arxiv.org/abs/2605.27392 |