Datasets:

jiosephlee
/

auxiliary-views-knowledge-acquisition

domain large_stringclasses 3 values	slug large_stringlengths 3 60	title large_stringlengths 15 112	authors listlengths 1 14	source_url large_stringlengths 32 50	publication_date large_stringclasses 9 values	upstream_license large_stringclasses 3 values	text large_stringlengths 10.5k 63k
arxiv	ByteLatent	Byte Latent Transformer: Patches Scale Better Than Tokens	[ "Artidoro Pagnoni", "Ram Pasunuru", "Pedro Rodriguez", "John Nguyen", "Benjamin Muller", "Margaret Li", "Chunting Zhou", "Lili Yu", "Jason Weston", "Luke Zettlemoyer", "Gargi Ghosh", "Mike Lewis", "Ari Holtzman", "Srinivasan Iyer" ]	https://arxiv.org/abs/2412.09871		CC BY 4.0	\title{Byte Latent Transformer: Patches Scale Better Than Tokens} \begin{abstract} We introduce the Byte Latent Transformer ({\textsc BLT}), a new byte-level LLM architecture that, for the first time, matches tokenization-based LLM performance at scale with significant improvements in inference efficiency and robustne...
arxiv	DPO	Direct Preference Optimization: Your Language Model is Secretly a Reward Model	[ "Rafael Rafailov", "Archit Sharma", "Eric Mitchell", "Stefano Ermon", "Christopher D. Manning", "Chelsea Finn" ]	https://arxiv.org/abs/2305.18290		CC BY 4.0	\title{Direct Preference Optimization: Your Language Model is Secretly a Reward Model} \begin{abstract} While large-scale unsupervised language models (LMs) learn broad world knowledge and some reasoning skills, achieving precise control of their behavior is difficult due to the completely unsupervised nature of their...
arxiv	FeatLLM	Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning	[ "Sungwon Han", "Jinsung Yoon", "Sercan O Arik", "Tomas Pfister" ]	https://arxiv.org/abs/2404.09491		CC BY 4.0	\title{Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning} \begin{abstract} Large Language Models (LLMs), with their remarkable ability to tackle challenging and unseen reasoning problems, hold immense potential for tabular learning, that is vital for many real-world applications. ...
arxiv	QLoRA	QLoRA: Efficient Finetuning of Quantized LLMs	[ "Tim Dettmers", "Artidoro Pagnoni", "Ari Holtzman", "Luke Zettlemoyer" ]	https://arxiv.org/abs/2305.14314		CC BY 4.0	"\\title{QLoRA: Efficient Finetuning of Quantized LLMs}\n\n\\begin{abstract}\nWe present \\textsc{QL(...TRUNCATED)
arxiv	fa3	FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision	[ "Jay Shah", "Ganesh Bikshandi", "Ying Zhang", "Vijay Thakkar", "Pradeep Ramani", "Tri Dao" ]	https://arxiv.org/abs/2407.08608		CC BY 4.0	"\\title{FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision}\n\n\\begin(...TRUNCATED)
arxiv	xLSTM	xLSTM: Extended Long Short-Term Memory	["Maximilian Beck","Korbinian Pöppel","Markus Spanring","Andreas Auer","Oleksandra Prudnikova","Mic(...TRUNCATED)	https://arxiv.org/abs/2405.04517		CC BY 4.0	"\\title{xLSTM: Extended Long Short-Term Memory}\n\n\\begin{abstract}\nIn the 1990s, the constant er(...TRUNCATED)
legal	America_First_Legal_Foundation_v_Jamieson_Greer	America First Legal Foundation v. Jamieson Greer	[ "U.S. Court of Appeals for the District of Columbia Circuit" ]	https://www.courtlistener.com/opinion/10690215/	2025-10-03	Public domain (U.S. judicial opinion)	"Title: America First Legal Foundation v. Jamieson Greer\nCourt: United States Court of Appeals for (...TRUNCATED)
legal	Apex_Bank_v_Cc_Serve_Corp	Apex Bank v. Cc Serve Corp.	[ "U.S. Court of Appeals for the Federal Circuit" ]	https://www.courtlistener.com/opinion/10677624/	2025-09-25	Public domain (U.S. judicial opinion)	"Title: Apex Bank v. CC Serve Corp.\nCourt: United States Court of Appeals for the Federal Circuit\n(...TRUNCATED)
legal	Bruce_Cohen_v_Consilio_LLC	Bruce Cohen v. Consilio, LLC	[ "U.S. Court of Appeals for the Eighth Circuit" ]	https://www.courtlistener.com/opinion/10691355/	2025-10-06	Public domain (U.S. judicial opinion)	"Title: Bruce Cohen v. Consilio, LLC\nCourt: United States Court of Appeals for the Eighth Circuit\n(...TRUNCATED)
legal	Finesse_Wireless_LLC_v_Att_Mobility_LLC	Finesse Wireless LLC v. At&t Mobility LLC	[ "U.S. Court of Appeals for the Federal Circuit" ]	https://www.courtlistener.com/opinion/10676767/	2025-09-24	Public domain (U.S. judicial opinion)	"Title: Finesse Wireless LLC v. AT&T Mobility LLC\nCourt: United States Court of Appeals for the Fed(...TRUNCATED)

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Auxiliary Views Knowledge Acquisition

This repository contains the cleaned source documents and evaluation probes used in Knowledge Acquisition During Pre-training? Large Language Models Learn Better With Auxiliary Views (arXiv:2609.04180).

News

August 21, 2026: Our paper was accepted to Findings of EMNLP 2026.

Configurations

Configuration	Split	Rows
`documents`	train	30
`factual_cloze`	test	6,435
`factual_mcqa_5shot`	test	4,515
`inference_cloze`	test	430
`inference_mcqa_5shot`	test	322

Both multiple-choice configurations include five-shot prompts in formatted_question_5shot.

Copyright and source documents

This is a mixed-rights repository; see LICENSE.md and ATTRIBUTION.md. The 30 released documents comprise:

6 arXiv papers licensed CC BY 4.0;
12 BMJ Case Reports articles licensed CC BY-NC 4.0; and
12 public-domain U.S. federal judicial opinions obtained via CourtListener.

Six cleaned arXiv texts are intentionally omitted:

Local document	arXiv ID	Reason
`1_58`	2402.17764	arXiv distribution license does not grant a general downstream redistribution right
`BOFT`	2311.06243	same
`GRPO`	2402.03300	same
`GSPO`	2507.18071	same
`OFT`	2306.07280	same
`LongRoPE`	2402.13753	CC BY-NC-ND does not permit redistribution of the cleaned/modified text

Probe rows for all 36 source documents are included. Short third-party excerpts associated with omitted documents are included for research and criticism under an asserted copyright exception/fair-use rationale and are expressly excluded from the license granted over project-authored work. This is a risk-managed research release, not legal advice.

Intended use and limitations

The probes are evaluation data, not training/validation splits. Medical source documents are case reports and must not be used for diagnosis, treatment, or clinical decision-making. The corpus is small, English-only, intentionally heterogeneous, and is not representative of any of its three domains.

Citation

@misc{lee2026knowledgeacquisitionpretraininglarge,
  title         = {Knowledge Acquisition During Pre-training? Large Language Models Learn Better With Auxiliary Views},
  author        = {Joseph Lee and Yidi Huang and Dokyoon Kim and Shu Yang and Li Shen},
  year          = {2026},
  eprint        = {2609.04180},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CL},
  url           = {https://arxiv.org/abs/2609.04180},
}

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Papers for jiosephlee/auxiliary-views-knowledge-acquisition

Knowledge Acquisition During Pre-training? Large Language Models Learn Better With Auxiliary Views

Paper • 2609.04180 • Published 15 days ago • 1

DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Paper • 2402.03300 • Published Feb 5, 2024 • 150