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Matthew Finlayson

Matthew Finlayson has 3 papers in our library, in 2022–2026, most of them on Language models & pretraining, Evaluation & benchmarks, Reasoning; often with Sean Welleck, Amanda Bertsch, Ashish Sabharwal.

3 papers in our library · 2022–2026

OpenAlex · ORCID · h-index 4 · 20 works · 233 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 441 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #5430

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

🎓University of Southern CaliforniaLos Angeles · United States2023–2026 · 9 paperscurrent🎓Southern States UniversitySan Diego · United States2026 · 1 papercurrent🔬Allen Institute for Artificial IntelligenceSeattle · United States2022 · 3 papers

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.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 3 worksLanguage and Linguistics · 3 worksArts and Humanities · 3 worksComputer Science · 3 worksMathematics · 1 workSafety Research · 1 workSocial Sciences · 1 work

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 2 worksEvaluation & benchmarks · 1 workReasoning · 1 work

 Works by year

2020s

20261 paper20241 paper20221 paper

 Papers

The most cited first (OpenAlex and our library).

  1. LILA: A Unified Benchmark for Mathematical Reasoning (2022 · 179 citations)
    Benchmarks that test one narrow math skill can make large language models look more capable than they are.
  2. From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models (2024 · 51 citations)
    Most LLM progress has been credited to spending more compute during training. The gains from spending compute at inference time have been studied by separate communities that use inconsistent terminology. This survey by Welleck and colleagues (CMU, USC, EleutherAI, Meta FAIR, University of Washington) organizes that…
  3. Side-by-side Comparison Amplifies Dialect Bias in Language Models (2026)
    The paper asks whether language models judge writers more harshly when their text is in African-American Vernacular English (AAVE) than when it is in Standard American English (SAE), even without any dialect label (covert dialect bias).

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. Decomposed Prompting: A Modular Approach for Solving Complex Tasks ↗ICLR 2023 (arXiv 2210.02406) · no summary yetTushar Khot et al.2023 · 390 citations
  2. LILA: A Unified Benchmark for Mathematical ReasoningConference on Empirical Methods in Natural Language Processing (EMNLP)Swaroop Mishra, Matthew Finlayson, Pan Lu, et al.2022 · 179 citations
  3. From Decoding to Meta-Generation: Inference-time Algorithms for Large Language ModelsarXiv:2406.16838Sean Welleck, Amanda Bertsch, Matthew Finlayson, et al.2024 · 51 citations
  4. Side-by-side Comparison Amplifies Dialect Bias in Language ModelsACM Conference on Fairness, Accountability, and Transparency (FAccT)Kritee Kondapally, Claire J. Smerdon, Pooja C. Patel, et al.2026

 Co-authors here

12 of 17 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 441 times (of the 149,439 papers whose bibliographies we read); 113 citing papers with a page here →.

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