Only Connect Walls Dataset Task 1 (Grouping)

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

1 leaderboard table shown for this task, 1 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

1 dataset whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

10 shown of 10 papers with code (10 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• RoBERTa: A Robustly Optimized BERT Pretraining Approach
  26 Jul 2019 67 repositories listed Syntology ran 22 of 48 samples · 26 unverified · 23 pointer-only (licence)
  Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging.
• Deep contextualized word representations
  15 Feb 2018 46 repositories listed Syntology ran 23 of 58 samples · 35 unverified · 25 pointer-only (licence)
  We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.
• DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
  2 Oct 2019 37 repositories listed Syntology ran 19 of 27 samples · 8 unverified
  As Transfer Learning from large-scale pre-trained models becomes more prevalent in Natural Language Processing (NLP), operating these large models in on-the-edge and/or under constrained computational training or…
• GPT-4 Technical Report
  15 Mar 2023 11 repositories listed Syntology ran 2 of 5 samples · 3 unverified · 1 pointer-only (licence)
  We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs.
• MPNet: Masked and Permuted Pre-training for Language Understanding
  20 Apr 2020 7 repositories listed Syntology ran 5 of 8 samples · 3 unverified · 6 pointer-only (licence)
  Since BERT neglects dependency among predicted tokens, XLNet introduces permuted language modeling (PLM) for pre-training to address this problem.
• GloVe: Global Vectors for Word Representation
  1 Oct 2014 4 repositories listed
• Learning Word Vectors for 157 Languages
  19 Feb 2018 2 repositories listed
  Distributed word representations, or word vectors, have recently been applied to many tasks in natural language processing, leading to state-of-the-art performance.
• Large Language Models are Fixated by Red Herrings: Exploring Creative Problem Solving and Einstellung Effect using the Only Connect Wall Dataset
  19 Jun 2023 1 repository listed Syntology ran 0 of 1 samples · 1 unverified
  In this paper we present the novel Only Connect Wall (OCW) dataset and report results from our evaluation of selected pre-trained language models and LLMs on creative problem solving tasks like grouping clue words by…
• Text Embeddings by Weakly-Supervised Contrastive Pre-training
  7 Dec 2022 1 repository listed
  This paper presents E5, a family of state-of-the-art text embeddings that transfer well to a wide range of tasks.
• Pre-Training of Deep Bidirectional Protein Sequence Representations with Structural Information
  25 Nov 2019 1 repository listed
  Bridging the exponentially growing gap between the numbers of unlabeled and labeled protein sequences, several studies adopted semi-supervised learning for protein sequence modeling.

Syntology lines on 6 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.