HellaSwag

Benchmarks archive 2025-07-28

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Libraries

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Datasets archive 2025-07-28

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Subtasks archive 2025-07-28

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Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

21 shown of 21 papers with code (39 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.

• Scaling Language Models: Methods, Analysis & Insights from Training Gopher
  8 Dec 2021 3 repositories listed
  Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world.
• Training Compute-Optimal Large Language Models
  29 Mar 2022 2 repositories listed Syntology ran 8 of 11 samples · 3 unverified · 4 pointer-only (licence)
  We investigate the optimal model size and number of tokens for training a transformer language model under a given compute budget.
• HellaSwag: Can a Machine Really Finish Your Sentence?
  19 May 2019 2 repositories listed Syntology ran 0 of 6 samples · 6 unverified · 4 pointer-only (licence)
  In this paper, we show that commonsense inference still proves difficult for even state-of-the-art models, by presenting HellaSwag, a new challenge dataset.
• Simulating Training Data Leakage in Multiple-Choice Benchmarks for LLM Evaluation
  30 May 2025 1 repository listed
  The performance of large language models (LLMs) continues to improve, as reflected in rising scores on standard benchmarks.
• DataDecide: How to Predict Best Pretraining Data with Small Experiments
  15 Apr 2025 1 repository listed Syntology ran 3 of 14 samples · 11 unverified
  Because large language models are expensive to pretrain on different datasets, using smaller-scale experiments to decide on data is crucial for reducing costs.
• What the HellaSwag? On the Validity of Common-Sense Reasoning Benchmarks
  10 Apr 2025 1 repository listed
  Measuring common-sense reasoning, therefore, is crucial for language models of different sizes and applications.
• FinerWeb-10BT: Refining Web Data with LLM-Based Line-Level Filtering
  13 Jan 2025 1 repository listed
  To test the impact of our filtering, we train GPT-2 models on both the original and the filtered datasets.
• LoRA Done RITE: Robust Invariant Transformation Equilibration for LoRA Optimization
  27 Oct 2024 1 repository listed Syntology ran 2 of 3 samples · 1 unverified · 3 pointer-only (licence)
  Low-rank adaption (LoRA) is a widely used parameter-efficient finetuning method for LLM that reduces memory requirements.
• You can remove GPT2's LayerNorm by fine-tuning
  6 Sep 2024 1 repository listed
  The LayerNorm (LN) layer in GPT-style transformer models has long been a hindrance to mechanistic interpretability.
• metabench -- A Sparse Benchmark to Measure General Ability in Large Language Models
  4 Jul 2024 1 repository listed Syntology ran 8 of 9 samples · 1 unverified · 9 pointer-only (licence)
  Large Language Models (LLMs) vary in their abilities on a range of tasks.
• LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding
  25 Apr 2024 1 repository listed Syntology ran 4 of 5 samples · 1 unverified · 5 pointer-only (licence)
  We present LayerSkip, an end-to-end solution to speed-up inference of large language models (LLMs).
• SaGE: Evaluating Moral Consistency in Large Language Models
  21 Feb 2024 1 repository listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  To this extent, we construct the Moral Consistency Corpus (MCC), containing 50K moral questions, responses to them by LLMs, and the RoTs that these models followed.
• Attacks on Node Attributes in Graph Neural Networks
  19 Feb 2024 1 repository listed
  Graphs are commonly used to model complex networks prevalent in modern social media and literacy applications.
• Gemini in Reasoning: Unveiling Commonsense in Multimodal Large Language Models
  29 Dec 2023 1 repository listed
  To address this gap, our study undertakes a thorough evaluation of Gemini's performance in complex reasoning tasks that necessitate the integration of commonsense knowledge across modalities.
• An Open Source Data Contamination Report for Large Language Models
  26 Oct 2023 1 repository listed
  We also introduce an open-source pipeline that enables the community to perform contamination analysis on customised data and models.
• In-Contextual Gender Bias Suppression for Large Language Models
  13 Sep 2023 1 repository listed
  We show that, using CrowsPairs dataset, our textual preambles covering counterfactual statements can suppress gender biases in English LLMs such as LLaMA2.
• Toward Adversarial Training on Contextualized Language Representation
  8 May 2023 1 repository listed
  Based on the observation, we propose simple yet effective \textit{Contextualized representation-Adversarial Training} (CreAT), in which the attack is explicitly optimized to deviate the contextualized representation of…
• GraDA: Graph Generative Data Augmentation for Commonsense Reasoning
  1 Oct 2022 1 repository listed
  Recent advances in commonsense reasoning have been fueled by the availability of large-scale human annotated datasets.
• On Curriculum Learning for Commonsense Reasoning
  1 Jul 2022 1 repository listed
  Hence, we examine the effect of a human-like easy-to-difficult curriculum during finetuning of language models for commonsense reasoning tasks.
• When Chosen Wisely, More Data Is What You Need: A Universal Sample-Efficient Strategy For Data Augmentation
  17 Mar 2022 1 repository listed
  From a pre-generated pool of augmented samples, Glitter adaptively selects a subset of worst-case samples with maximal loss, analogous to adversarial DA.
• UNICORN on RAINBOW: A Universal Commonsense Reasoning Model on a New Multitask Benchmark
  24 Mar 2021 1 repository listed Syntology ran 0 of 5 samples · 5 unverified
  First, we propose a new multitask benchmark, RAINBOW, to promote research on commonsense models that generalize well over multiple tasks and datasets.

Syntology lines on 8 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.