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2012.13575
Cited By

Contextual Temperature for Language Modeling

Contextual Temperature for Language Modeling

25 December 2020

Sheng-Iou Hsieh

Shih-Chieh Chang

ArXiv (abs)PDF HTML

Papers citing "Contextual Temperature for Language Modeling"

19 / 19 papers shown

Improving Local Training in Federated Learning via Temperature Scaling

Improving Local Training in Federated Learning via Temperature Scaling

JeongGil Ko

286

5

0

24 Dec 2025

Temperature in SLMs: Impact on Incident Categorization in On-Premises Environments

Temperature in SLMs: Impact on Incident Categorization in On-Premises Environments

Marcio Pohlmann

Lourenço Pereira

79

1

0

21 Nov 2025

Certified Self-Consistency: Statistical Guarantees and Test-Time Training for Reliable Reasoning in LLMs

Certified Self-Consistency: Statistical Guarantees and Test-Time Training for Reliable Reasoning in LLMs

Paula Cordero-Encinar

232

3

0

20 Oct 2025

Reasoning with Sampling: Your Base Model is Smarter Than You Think

Reasoning with Sampling: Your Base Model is Smarter Than You Think

ReLM OffRL SyDa AI4TS LRM

277

35

0

16 Oct 2025

Posterior-GRPO: Rewarding Reasoning Processes in Code Generation

Posterior-GRPO: Rewarding Reasoning Processes in Code Generation

241

19

0

07 Aug 2025

TreeSynth: Synthesizing Diverse Data from Scratch via Tree-Guided Subspace Partitioning

TreeSynth: Synthesizing Diverse Data from Scratch via Tree-Guided Subspace Partitioning

540

2

0

21 Mar 2025

A statistically consistent measure of semantic uncertainty using Language Models

A statistically consistent measure of semantic uncertainty using Language Models

416

0

0

01 Feb 2025

From Text to Pose to Image: Improving Diffusion Model Control and
Quality

From Text to Pose to Image: Improving Diffusion Model Control and Quality

Clément Bonnet

355

2

0

19 Nov 2024

Synthesize, Partition, then Adapt: Eliciting Diverse Samples from
Foundation Models

Synthesize, Partition, then Adapt: Eliciting Diverse Samples from Foundation ModelsNeural Information Processing Systems (NeurIPS), 2024

Swarat Chaudhuri

297

1

0

11 Nov 2024

Can Large Language Models Invent Algorithms to Improve Themselves?: Algorithm Discovery for Recursive Self-Improvement through Reinforcement Learning

Can Large Language Models Invent Algorithms to Improve Themselves?: Algorithm Discovery for Recursive Self-Improvement through Reinforcement Learning

Yoichi Ishibashi

Masafumi Oyamada

545

1

0

21 Oct 2024

Cognitive Overload Attack:Prompt Injection for Long Context

Cognitive Overload Attack:Prompt Injection for Long Context

Bibek Upadhayay

351

15

0

15 Oct 2024

Non-Determinism of "Deterministic" LLM Settings

Non-Determinism of "Deterministic" LLM Settings

...

Tomasz Tudrej

Ferhan Ture

Zhe Wu

Lixinyu Xu

Breck Baldwin

1.1K

0

0

06 Aug 2024

Large Language Models have Intrinsic Self-Correction Ability

Large Language Models have Intrinsic Self-Correction Ability

Amir Nassereldine

Jinjun Xiong

286

36

0

21 Jun 2024

Adaptive Preference Scaling for Reinforcement Learning with Human
Feedback

Adaptive Preference Scaling for Reinforcement Learning with Human Feedback

Alexander Bukharin

Yixiao Li

Tuo Zhao

273

15

0

04 Jun 2024

To Cool or not to Cool? Temperature Network Meets Large Foundation
Models via DRO

To Cool or not to Cool? Temperature Network Meets Large Foundation Models via DRO

335

11

0

06 Apr 2024

The Effect of Sampling Temperature on Problem Solving in Large Language
Models

The Effect of Sampling Temperature on Problem Solving in Large Language Models

321

248

0

07 Feb 2024

Fine-tune your Classifier: Finding Correlations With Temperature

Fine-tune your Classifier: Finding Correlations With TemperatureInternational Conference on Information Photonics (ICIP), 2022

Benjamin Chamand

Olivier Risser-Maroix

Nicolas Loménie

340

2

0

18 Oct 2022

Learning Natural Language Generation from Scratch

Learning Natural Language Generation from Scratch

Alice Martin Donati

Guillaume Quispe

Sylvain Le Corff

Olivier Pietquin

186

4

0

20 Sep 2021

Straight to the Gradient: Learning to Use Novel Tokens for Neural Text
Generation

Straight to the Gradient: Learning to Use Novel Tokens for Neural Text GenerationInternational Conference on Machine Learning (ICML), 2021

146

30

0

14 Jun 2021

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