--- title: "Samuel R. Bowman — papers, page 2 — Elamur Research" description: "Samuel R. Bowman: 31 papers on Elamur Research (2013–2023), h-index 48 — affiliations, topics, co-authors and who cites them in our library." canonical: "https://research.elamur.ai/free/en/authors/samuel-r-bowman/page/2/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/samuel-r-bowman/page/2/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Samuel R. Bowman](https://md.research.elamur.ai/free/en/authors/samuel-r-bowman/) / page 2 # Samuel R. Bowman · page 2 31 papers in our library · 2013–2023 · page 2 of 2 [The profile, affiliations and co-authors →](https://md.research.elamur.ai/free/en/authors/samuel-r-bowman/) ## Papers The most cited first (OpenAlex and our library). [← prev](https://md.research.elamur.ai/free/en/authors/samuel-r-bowman/) [1](https://md.research.elamur.ai/free/en/authors/samuel-r-bowman/) **2** 26. **[Counterfactually-Augmented SNLI Training Data Does Not Yield Better Generalization Than Unaugmented Data](https://md.research.elamur.ai/free/en/papers/huang-2020-counterfactually-augmented-snli-training-data/)** *(2020 · 51 citations)* Crowdsourced NLI benchmarks contain shortcuts such as hypothesis-only cues, negation words and lexical overlap. 27. **[More Constructions, More Genres: Extending Stanford Dependencies](https://md.research.elamur.ai/free/en/papers/marneffe-2013-more-constructions-more-genres-extending/)** *(2013 · 47 citations)* The Stanford scheme was built on newswire and gives little analysis to constructions that are rare there but common in web text: tough adjectives, free relatives, comparatives, small clauses, questions, imperatives, fragments and lists. 28. **[NOPE: A Corpus of Naturally-Occurring Presuppositions in English](https://md.research.elamur.ai/free/en/papers/parrish-2021-nope-corpus-naturally-occurring-presuppositions/)** *(2021 · 40 citations)* NOPE collects about 1,280 corpus passages, each with a presupposition trigger of one of ten types: change of state, aspectual verbs (begin, stop), clause-embedding verbs (know, realize, say), clefts, comparatives, embedded questions, implicatives (manage to, fail to), numeric determiners (both), re- verbs, and… 29. **[New Protocols and Negative Results for Textual Entailment Data Collection](https://md.research.elamur.ai/free/en/papers/bowman-2020-new-protocols-negative-results-textual/)** *(2020 · 27 citations)* Data from natural language inference (NLI) is widely used for intermediate training before fine-tuning on other understanding tasks. 30. **[Two Failures of Self-Consistency in the Multi-Step Reasoning of LLMs](https://md.research.elamur.ai/free/en/papers/chen-2023-two-failures-self-consistency-multi/)** *(2023 · 25 citations)* Evaluations of LLMs usually check whether answers are correct, not whether a model's answers agree with each other. 31. **[BLiMP: The Benchmark of Linguistic Minimal Pairs for English](https://md.research.elamur.ai/free/en/papers/warstadt-2020-blimp-benchmark-linguistic-minimal-pairs/)** *(2020 · 21 citations)* BLiMP is a challenge set for measuring which parts of English grammar language models have learned. It contains 67 paradigms of 1,000 sentence pairs each; within a pair the two sentences differ minimally and only one is acceptable. The pairs are generated automatically from templates designed by linguists and cover… [← prev](https://md.research.elamur.ai/free/en/authors/samuel-r-bowman/) [1](https://md.research.elamur.ai/free/en/authors/samuel-r-bowman/) **2**