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Bernal Jimenez Gutierrez

Bernal Jimenez Gutierrez has 3 papers in our library, in 2022–2024, most of them on Language models & pretraining; often with Yu Su, Clayton Washington, Huan Sun.

3 papers in our library · 2022–2024

OpenAlex · h-index 4 · 15 works · 254 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 332 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #3170

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓The Ohio State UniversityColumbus · United States2020–2026 · 9 paperscurrent🎓Johns Hopkins UniversityBaltimore · United States2026 · 2 paperscurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 more institutions on single bylines are not listed.

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📚 Shelves 9The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 3 worksCognitive Neuroscience · 1 workNeuroscience · 1 workBiochemistry, Genetics and Molecular Biology · 1 workMolecular Biology · 1 workComputer Vision and Pattern Recognition · 1 work

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 3 works

 Works by year

2020s

20241 paperHippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models20231 paperAligning Instruction Tasks Unlocks Large Language Models as Zero-Shot Relation Extractors20221 paperThinking about GPT-3 In-Context Learning for Biomedical IE? Think Again

 Papers

The most cited first (OpenAlex and our library).

  1. HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models (2024 · 264 citations)
    Standard retrieval-augmented generation (RAG) encodes each passage on its own. This makes it hard for LLMs to answer questions that require connecting facts scattered across several documents. HippoRAG addresses this with a design based on the hippocampal memory indexing theory. An instruction-tuned LLM plays the…
  2. Thinking about GPT-3 In-Context Learning for Biomedical IE? Think Again (2022 · 179 citations)
    Annotating biomedical text for information extraction is expensive, so GPT-3's ability to learn from a few prompt examples looks attractive.
  3. Aligning Instruction Tasks Unlocks Large Language Models as Zero-Shot Relation Extractors (2023 · 118 citations)
    Instruction-tuned LLMs perform well on many NLP tasks without examples, yet they still lose to compact models on relation extraction (RE), the task of naming how two entities in a sentence are connected.

All works

4 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

  1. HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language ModelsConference on Neural Information Processing Systems (NeurIPS)Bernal Jimenez Gutierrez, Yiheng Shu, Yu Gu, et al.2024 · 264 citations
  2. Thinking about GPT-3 In-Context Learning for Biomedical IE? Think AgainFindings of the Association for Computational LinguisticsBernal Jimenez Gutierrez, Nikolas McNeal, Clayton Washington, et al.2022 · 179 citations
  3. Aligning Instruction Tasks Unlocks Large Language Models as Zero-Shot Relation ExtractorsFindings of the Association for Computational LinguisticsKai Zhang, Bernal Jimenez Gutierrez, Yu Su2023 · 118 citations
  4. From RAG to Memory: Non-Parametric Continual Learning for Large Language ModelsarXiv:2502.14802 · original ↗ · no summary yetBernal Jimenez Gutierrez et al.2025 · 88 citations

 Co-authors here

6 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 332 times (of the 141,840 papers whose bibliographies we read); 11 citing papers with a page here →.

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