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Niklas Muennighoff

8 papers on this site · 2022–2025

current affiliationStanford University, Stanford, United States

Niklas Muennighoff has 8 papers on this site, in 2022–2025, most of them on Language models & pretraining, Multilingual & cross-lingual NLP, Evaluation & benchmarks; often with Adam Roberts, Alex Gu, Amanpreet Singh.

OpenAlex · ORCID · h-index 24 · 103 works · 4 251 citations OpenAlex, 2026-10-14

8 works on this site · 5 652 citations in our library · 13 more works in our library

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

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

Topics

Language models & pretraining3Multilingual & cross-lingual NLP1Evaluation & benchmarks1Vision & language1

Papers

The most cited first (OpenAlex and our library).

  1. MTEB: Massive Text Embedding Benchmark (2022 · 1 021 citations)
    Text embedding models are usually tested on only a few datasets, most often semantic textual similarity (STS).
  2. C-Pack: Packed Resources For General Chinese Embeddings (2023 · 920 citations)
    Chinese has lagged behind English in general-purpose text embeddings because it lacks large public training sets, competitive models and a broad evaluation suite.
  3. Crosslingual Generalization through Multitask Finetuning (2022 · 692 citations)
    Multitask prompted finetuning (MTF) helps large language models handle unseen tasks zero-shot, but earlier studies stayed almost entirely within English.
  4. s1: Simple test-time scaling (2025 · 584 citations)
    The paper looks for the least machinery needed to reproduce what OpenAI's o1 showed: a model that gets more accurate when it is allowed to think longer at inference time.
  5. SGPT: GPT Sentence Embeddings for Semantic Search (2022 · 187 citations)
    Large GPT-style decoder models lead many language tasks, but semantic search and sentence embeddings have remained the domain of much smaller BERT-like encoders.
  6. Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models (2024 · 184 citations)
    The strongest vision-language models (VLMs) are closed. Most capable open-weight alternatives are trained on captions or answers produced by proprietary systems such as GPT-4V. In effect they distill closed models, so it remains unclear how to build a strong VLM from scratch. The authors release Molmo, a family of…
  7. Generative Representational Instruction Tuning (2024 · 165 citations)
    LLMs are usually specialised. A model tuned for text generation produces weak embeddings, and a model tuned for embeddings loses its ability to generate. This paper introduces GRIT (generative representational instruction tuning), which trains one LLM on both kinds of data. The instruction format tells the model…
  8. SantaCoder: don't reach for the stars! (2023 · 164 citations)
    Most code LLMs are built with little transparency: training data, preprocessing, and design choices are rarely disclosed.

Also in our library

13 more works of theirs are in our library without a page here yet; they will appear as we publish them.

Co-authors here

12 of 42 co-authors with a page, the most papers together first.

Cited by

Papers of our library cite their works 5 652 times (of the 140 884 papers whose bibliographies we read); 282 citing papers with a page here →.

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