Hyperdecoders: Instance-specific decoders for multi-task NLP. - Hamish Ivison; Matthew E. Peters (EMNLP (Findings), 2022)

HINT: Hypernetwork Instruction Tuning for Efficient Zero-Shot Generalisation. - Hamish Ivison; Akshita Bhagia; Yizhong Wang; Hannaneh Hajishirzi; Matthew E. Peters (CoRR, 2022)

HINT: Hypernetwork Instruction Tuning for Efficient Zero- and Few-Shot Generalisation. - Hamish Ivison; Akshita Bhagia; Yizhong Wang; Hannaneh Hajishirzi; Matthew E. Peters (ACL (1), 2023)

Data-Efficient Finetuning Using Cross-Task Nearest Neighbors. - Hamish Ivison; Noah A. Smith; Hannaneh Hajishirzi; Pradeep Dasigi (ACL (Findings), 2023)

How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources. - Yizhong Wang; Hamish Ivison; Pradeep Dasigi; Jack Hessel; Tushar Khot; Khyathi Raghavi Chandu; David Wadden; Kelsey MacMillan; Noah A. Smith; Iz Beltagy; Hannaneh Hajishirzi (NeurIPS, 2023)

Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2. - Hamish Ivison; Yizhong Wang; Valentina Pyatkin; Nathan Lambert 001; Matthew E. Peters; Pradeep Dasigi; Joel Jang; David Wadden; Noah A. Smith; Iz Beltagy; Hannaneh Hajishirzi (CoRR, 2023)

OLMo: Accelerating the Science of Language Models. - Dirk Groeneveld; Iz Beltagy; Evan Pete Walsh; Akshita Bhagia; Rodney Kinney; Oyvind Tafjord; Ananya Harsh Jha; Hamish Ivison; Ian Magnusson; Yizhong Wang; Shane Arora; David Atkinson; et al. (ACL (1), 2024)

TESS: Text-to-Text Self-Conditioned Simplex Diffusion. - Rabeeh Karimi Mahabadi; Hamish Ivison; Jaesung Tae; James Henderson 001; Iz Beltagy; Matthew E. Peters; Arman Cohan (EACL (1), 2024)

Backtracking Mathematical Reasoning of Language Models to the Pretraining Data. - Yasaman Razeghi; Hamish Ivison; Sameer Singh 001; Yanai Elazar (Tiny Papers @ ICLR, 2024)

Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback. - Hamish Ivison; Yizhong Wang; Jiacheng Liu 010; Zeqiu Wu; Valentina Pyatkin; Nathan Lambert 001; Noah A. Smith; Yejin Choi 001; Hanna Hajishirzi (NeurIPS, 2024)

Personalizing Reinforcement Learning from Human Feedback with Variational Preference Learning. - Sriyash Poddar; Yanming Wan; Hamish Ivison; Abhishek Gupta; Natasha Jaques (NeurIPS, 2024)

OLMo: Accelerating the Science of Language Models. - Dirk Groeneveld; Iz Beltagy; Pete Walsh; Akshita Bhagia; Rodney Kinney; Oyvind Tafjord; Ananya Harsh Jha; Hamish Ivison; Ian Magnusson; Yizhong Wang; Shane Arora; David Atkinson; et al. (CoRR, 2024)

TÜLU 3: Pushing Frontiers in Open Language Model Post-Training. - Nathan Lambert 001; Jacob Morrison; Valentina Pyatkin; Shengyi Huang; Hamish Ivison; Faeze Brahman; Lester James V. Miranda; Alisa Liu; Nouha Dziri; Shane Lyu; Yuling Gu; Saumya Malik; et al. (CoRR, 2024)

Local Interpretations for Explainable Natural Language Processing: A Survey. - Siwen Luo; Hamish Ivison; Soyeon Caren Han; Josiah Poon (ACM Comput. Surv., 2024)

TESS 2: A Large-Scale Generalist Diffusion Language Model. - Jaesung Tae; Hamish Ivison; Sachin Kumar; Arman Cohan (ACL (1), 2025)

2 OLMo 2 Furious. - Team OLMo; Pete Walsh; Luca Soldaini; Dirk Groeneveld; Kyle Lo; Shane Arora; Akshita Bhagia; Yuling Gu; Shengyi Huang; Matt Jordan; Nathan Lambert 001; Dustin Schwenk; et al. (CoRR, 2025)

Large-Scale Data Selection for Instruction Tuning. - Hamish Ivison; Muru Zhang; Faeze Brahman; Pang Wei Koh; Pradeep Dasigi (CoRR, 2025)

Generalizing Verifiable Instruction Following. - Valentina Pyatkin; Saumya Malik; Victoria Graf; Hamish Ivison; Shengyi Huang; Pradeep Dasigi; Nathan Lambert 001; Hannaneh Hajishirzi (CoRR, 2025)

The Delta Learning Hypothesis: Preference Tuning on Weak Data can Yield Strong Gains. - Scott Geng; Hamish Ivison; Chun-Liang Li; Maarten Sap; Jerry Li; Ranjay Krishna; Pang Wei Koh (CoRR, 2025)

The Curious Case of Factuality Finetuning: Models' Internal Beliefs Can Improve Factuality. - Benjamin Newman; Abhilasha Ravichander; Jaehun Jung; Rui Xin; Hamish Ivison; Yegor Kuznetsov; Pang Wei Koh; Yejin Choi 001 (CoRR, 2025)