Optimising Language Models for Downstream Tasks: A Post-Training Perspective | Paper Citation Record

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This paper cites Nemotron-4 340B Technical Report.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Nemotron-4 340B Technical Report

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This paper cites Publicly available clinical BERT embeddings.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Publicly available clinical BERT embeddings

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This paper cites Reid, Stephen Gould, and Anton van den Hengel.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Reid, Stephen Gould, and Anton van den Hengel

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This paper cites Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning

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This paper cites Tran, Dara Bahri, Jianmo Ni, Jai Prakash Gupta, Kai Hui, Sebastian Ruder, and Donald Metzler.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Tran, Dara Bahri, Jianmo Ni, Jai Prakash Gupta, Kai Hui, Sebastian Ruder, and Donald Metzler

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This paper cites A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings

Reference 7

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This paper cites ATTEMPT: Parameter-efficient multi-task tuning via attentional mixtures of soft prompts.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective ATTEMPT: Parameter-efficient multi-task tuning via attentional mixtures of soft prompts

Reference 8

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This paper cites Program Synthesis with Large Language Models.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Program Synthesis with Large Language Models

Reference 9

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This paper cites A general theoret- ical paradigm to understand learning from human preferences.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective A general theoret- ical paradigm to understand learning from human preferences

Reference 10

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This paper cites Qwen Technical Report.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Qwen Technical Report

Reference 11

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This paper cites Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 12

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This paper cites Brain power.Proceedings of the National Academy of Sciences, 118(32):e2107022118, 2021.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Brain power.Proceedings of the National Academy of Sciences, 118(32):e2107022118, 2021

Reference 13

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This paper cites SciBERT: A pretrained language model for scientific text.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective SciBERT: A pretrained language model for scientific text

Reference 14

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This paper cites BitFit: Sim- ple parameter-efficient fine-tuning for transformer-based masked language- models.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective BitFit: Sim- ple parameter-efficient fine-tuning for transformer-based masked language- models

Reference 15

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This paper cites Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell

Reference 16

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This paper cites Bender and Alexander Koller.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Bender and Alexander Koller

Reference 17

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This paper cites Curriculum learning.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Curriculum learning

Reference 18

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This paper cites The fifth PASCAL recognizing textual entailment challenge.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective The fifth PASCAL recognizing textual entailment challenge

Reference 19

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This paper cites Cubuk, Alex Kurakin, Kihyuk Sohn, Han Zhang, and Colin Raffel.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Cubuk, Alex Kurakin, Kihyuk Sohn, Han Zhang, and Colin Raffel

Reference 20

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This paper cites Goodfellow, Nicolas Papernot, Avital Oliver, and Colin Raffel.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Goodfellow, Nicolas Papernot, Avital Oliver, and Colin Raffel

Reference 21

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This paper cites Adamatch: A unified approach to semi-supervised learning and domain adaptation.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Adamatch: A unified approach to semi-supervised learning and domain adaptation

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This paper cites Shih, Yejin Choi, and Daniel Marcu.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Shih, Yejin Choi, and Daniel Marcu

Reference 23

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This paper cites PIQA: reasoning about physical commonsense in natural language.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective PIQA: reasoning about physical commonsense in natural language

Reference 24

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This paper cites Bowman, Gabor Angeli, Christopher Potts, and Christopher D.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Bowman, Gabor Angeli, Christopher Potts, and Christopher D

Reference 25

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Optimising Language Models for Downstream Tasks: A Post-Training Perspective Unresolved cited work

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This paper cites Semi-supervised semantic role labeling with cross-view training.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Semi-supervised semantic role labeling with cross-view training

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This paper cites Findings of the 2009 Workshop on Statistical Machine Translation.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Findings of the 2009 Workshop on Statistical Machine Translation

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This paper cites Extracting training data from large language models.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Extracting training data from large language models

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This paper cites SemEval-2017 task 1: Semantic textual similarity multilingual and crosslingual focused evaluation.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective SemEval-2017 task 1: Semantic textual similarity multilingual and crosslingual focused evaluation

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This paper cites Importance of semantic representation: dataless classification.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Importance of semantic representation: dataless classification

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This paper cites Semi-supervised BIBLIOGRAPHY 121 learning (chapelle, o.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Semi-supervised BIBLIOGRAPHY 121 learning (chapelle, o

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This paper cites Code alpaca: An instruction-following llama model for code generation.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Code alpaca: An instruction-following llama model for code generation

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This paper cites Debiased self-training for semi-supervised learning.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Debiased self-training for semi-supervised learning

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Optimising Language Models for Downstream Tasks: A Post-Training Perspective Unseen filler generalization in attention-based natural language reasoning models

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Optimising Language Models for Downstream Tasks: A Post-Training Perspective TOUCHDOWN: natural language navigation and spatial reasoning in visual street environments

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This paper cites MixText: Linguistically-informed interpolation of hidden space for semi-supervised text classification.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective MixText: Linguistically-informed interpolation of hidden space for semi-supervised text classification

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Optimising Language Models for Downstream Tasks: A Post-Training Perspective Alpagasus: Training a better alpaca model with fewer data

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This paper cites Evaluating Large Language Models Trained on Code.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Evaluating Large Language Models Trained on Code

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This paper cites Microsoft COCO Captions: Data Collection and Evaluation Server.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Microsoft COCO Captions: Data Collection and Evaluation Server

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This paper cites Adapt- ing language models to compress contexts.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Adapt- ing language models to compress contexts

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Optimising Language Models for Downstream Tasks: A Post-Training Perspective Gonza- lez, Ion Stoica, and Eric P

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Optimising Language Models for Downstream Tasks: A Post-Training Perspective PaLM: Scaling Language Modeling with Pathways

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This paper cites Deep reinforcement learning from human preferences.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Deep reinforcement learning from human preferences

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This paper cites Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V

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This paper cites BoolQ: Exploring the surprising difficulty of natural yes/no questions.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective BoolQ: Exploring the surprising difficulty of natural yes/no questions

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Optimising Language Models for Downstream Tasks: A Post-Training Perspective Manning, and Quoc Le

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This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

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This paper cites Training verifiers to solve math word problems, 2021.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Training verifiers to solve math word problems, 2021

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This paper cites Free dolly: Introducing the world’s first truly open instruction-tuned llm, 2023.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Free dolly: Introducing the world’s first truly open instruction-tuned llm, 2023

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Optimising Language Models for Downstream Tasks: A Post-Training Perspective The PASCAL recog- nising textual entailment challenge

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Optimising Language Models for Downstream Tasks: A Post-Training Perspective Flashattention-2: Faster attention with better parallelism and work partitioning, 2023

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Optimising Language Models for Downstream Tasks: A Post-Training Perspective The commitmentbank: Investigating projection in naturally occurring discourse

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Optimising Language Models for Downstream Tasks: A Post-Training Perspective Universal transformers

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Optimising Language Models for Downstream Tasks: A Post-Training Perspective BERT: Pre-training of deep bidirectional transformers for language understanding

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This paper cites Attention over learned object embeddings enables complex visual reasoning.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Attention over learned object embeddings enables complex visual reasoning

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Optimising Language Models for Downstream Tasks: A Post-Training Perspective Dolan and Chris Brockett

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This paper cites A robust self-learning framework for cross- lingual text classification.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective A robust self-learning framework for cross- lingual text classification

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This paper cites The Llama 3 Herd of Models.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective The Llama 3 Herd of Models

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This paper cites SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine

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This paper cites MRQA 2019 shared task: Evaluating generalization in reading com- prehension.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective MRQA 2019 shared task: Evaluating generalization in reading com- prehension

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Optimising Language Models for Downstream Tasks: A Post-Training Perspective Making pre-trained language models better few-shot learners

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Optimising Language Models for Downstream Tasks: A Post-Training Perspective Zero-shot text classification with self-training

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Optimising Language Models for Downstream Tasks: A Post-Training Perspective The third PASCAL recognizing textual entailment challenge

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Optimising Language Models for Downstream Tasks: A Post-Training Perspective PARS: Pseudo-Label Aware Robust Sample Selection for Learning with Noisy Labels

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Optimising Language Models for Downstream Tasks: A Post-Training Perspective Making the V in VQA matter: Elevating the role of image under- standing in visual question answering

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Optimising Language Models for Downstream Tasks: A Post-Training Perspective Semi-supervised learning by en- tropy minimization

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Optimising Language Models for Downstream Tasks: A Post-Training Perspective Projected language models: A large model pre-segmented into smaller ones

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Optimising Language Models for Downstream Tasks: A Post-Training Perspective PPT: Pre-trained prompt tuning for few-shot learning

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Optimising Language Models for Downstream Tasks: A Post-Training Perspective Textbooks Are All You Need

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Optimising Language Models for Downstream Tasks: A Post-Training Perspective Parameter-efficient transfer learning with diff pruning

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Optimising Language Models for Downstream Tasks: A Post-Training Perspective W ARP: Word-level Adversarial ReProgramming

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Optimising Language Models for Downstream Tasks: A Post-Training Perspective ToxiGen: A large-scale machine-generated dataset for adversarial and implicit hate speech detection

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Optimising Language Models for Downstream Tasks: A Post-Training Perspective Preserving Pre-trained Features Helps Calibrate Fine-tuned Language Models

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Optimising Language Models for Downstream Tasks: A Post-Training Perspective Extending clip for category-to-image re- trieval in e-commerce

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Optimising Language Models for Downstream Tasks: A Post-Training Perspective Aligning AI with shared human values

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Optimising Language Models for Downstream Tasks: A Post-Training Perspective Measuring massive multitask language BIBLIOGRAPHY 129 understanding

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Optimising Language Models for Downstream Tasks: A Post-Training Perspective Training Compute-Optimal Large Language Models

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Optimising Language Models for Downstream Tasks: A Post-Training Perspective ORPO: Monolithic Preference Optimization without Reference Model

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Optimising Language Models for Downstream Tasks: A Post-Training Perspective Unnatural in- structions: Tuning language models with (almost) no human labor

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Optimising Language Models for Downstream Tasks: A Post-Training Perspective Human feedback is not gold standard

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This paper cites Meta-learning the differ- ence: Preparing large language models for efficient adaptation.Transactions of the Association for Computational Linguistics, 10:1249–1265, 2022.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Meta-learning the differ- ence: Preparing large language models for efficient adaptation.Transactions of the Association for Computational Linguistics, 10:1249–1265, 2022

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Optimising Language Models for Downstream Tasks: A Post-Training Perspective Parameter-efficient transfer learning for NLP

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Optimising Language Models for Downstream Tasks: A Post-Training Perspective Universal language model fine-tuning for text classification

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Optimising Language Models for Downstream Tasks: A Post-Training Perspective Can llms learn from a single exam- ple?, 2023

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Optimising Language Models for Downstream Tasks: A Post-Training Perspective Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen

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Optimising Language Models for Downstream Tasks: A Post-Training Perspective Mining and summarizing customer reviews

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Optimising Language Models for Downstream Tasks: A Post-Training Perspective Instruction fine-tuning: Does prompt loss mat- ter?, 2024

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Optimising Language Models for Downstream Tasks: A Post-Training Perspective Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations

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Optimising Language Models for Downstream Tasks: A Post-Training Perspective Hyperdecoders: Instance-specific de- coders for multi-task NLP

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Optimising Language Models for Downstream Tasks: A Post-Training Perspective Camels in a changing climate: Enhancing lm adaptation with tulu 2, 2023

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Optimising Language Models for Downstream Tasks: A Post-Training Perspective Bartoldson, Bhavya Kailkhura, Avi Schwarzschild, Aniruddha Saha, Micah Goldblum, Jonas Geiping, and Tom Goldstein

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Optimising Language Models for Downstream Tasks: A Post-Training Perspective Representation learning for grounded spatial reasoning.Transactions of the Association for Computational Linguistics, 6:49–61, 2018

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Optimising Language Models for Downstream Tasks: A Post-Training Perspective LIMIT: Less Is More for Instruction Tuning Across Evaluation Paradigms

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Observation 013171b2-3b55-4642-b22c-d29254e49371 · outbound

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Optimising Language Models for Downstream Tasks: A Post-Training Perspective Scaling Laws for Neural Language Models

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Optimising Language Models for Downstream Tasks: A Post-Training Perspective Parameter-efficient multi-task fine-tuning for transformers via shared hypernetworks

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Optimising Language Models for Downstream Tasks: A Post-Training Perspective Looking beyond the surface: A challenge set for reading compre- hension over multiple sentences

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Optimising Language Models for Downstream Tasks: A Post-Training Perspective UNIFIEDQA: Crossing for- mat boundaries with a single QA system

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Optimising Language Models for Downstream Tasks: A Post-Training Perspective Scitail: A textual entail- ment dataset from science question answering.Proceedings of the AAAI Conference on Artificial Intelligence, 32(1), Apr

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