Electrically Tunable Superconducting Microstrip Line Band-Pass Filter for Mobile Applications. - Hiroyuki Kayano; Hiroyuki Fuke; Fumihiko Aiga; Mutsuki Yamazaki; Yoshiaki Terashima; Tatsunori Hashimoto (IEICE Trans. Electron., 2005)

BFL: a node and edge betweenness based fast layout algorithm for large scale networks. - Tatsunori B. Hashimoto; Masao Nagasaki; Kaname Kojima; Satoru Miyano (BMC Bioinform., 2009)

Superconducting Narrowband Filter for Receiver of Weather Radar. - Tamio Kawaguchi; Noritsugu Shiokawa; Kohei Nakayama; Takatoshi Watanabe; Tatsunori Hashimoto; Hiroyuki Kayano (IEICE Trans. Electron., 2009)

Tree preserving embedding. - Albert Shieh; Tatsunori B. Hashimoto; Edoardo M. Airoldi (ICML, 2011)

Lineage-based identification of cellular states and expression programs. - Tatsunori B. Hashimoto; Tommi S. Jaakkola; Richard Sherwood; Esteban O. Mazzoni; Hynek Wichterle; David K. Gifford (Bioinform., 2012)

Universal Count Correction for High-Throughput Sequencing. - Tatsunori B. Hashimoto; Matthew D. Edwards; David K. Gifford (PLoS Comput. Biol., 2014)

Metric recovery from directed unweighted graphs. - Tatsunori B. Hashimoto; Yi Sun 010; Tommi S. Jaakkola (AISTATS, 2015)

From random walks to distances on unweighted graphs. - Tatsunori B. Hashimoto; Yi Sun 010; Tommi S. Jaakkola (NIPS, 2015)

Word, graph and manifold embedding from Markov processes. - Tatsunori B. Hashimoto; David Alvarez-Melis; Tommi S. Jaakkola (CoRR, 2015)

Learning Population-Level Diffusions with Generative RNNs. - Tatsunori B. Hashimoto; David K. Gifford; Tommi S. Jaakkola (ICML, 2016)

GERV: a statistical method for generative evaluation of regulatory variants for transcription factor binding. - Haoyang Zeng; Tatsunori B. Hashimoto; Daniel D. Kang; David K. Gifford (Bioinform., 2016)

Word Embeddings as Metric Recovery in Semantic Spaces. - Tatsunori B. Hashimoto; David Alvarez-Melis; Tommi S. Jaakkola (Trans. Assoc. Comput. Linguistics, 2016)

Continuous representations and models from random walk diffusion limits. - Tatsunori Benjamin Hashimoto (2016)

Unsupervised Transformation Learning via Convex Relaxations. - Tatsunori B. Hashimoto; Percy Liang; John C. Duchi (NIPS, 2017)

Derivative Free Optimization Via Repeated Classification. - Tatsunori Hashimoto; Steve Yadlowsky; John C. Duchi (AISTATS, 2018)

Fairness Without Demographics in Repeated Loss Minimization. - Tatsunori B. Hashimoto; Megha Srivastava; Hongseok Namkoong; Percy Liang (ICML, 2018)

A Retrieve-and-Edit Framework for Predicting Structured Outputs. - Tatsunori B. Hashimoto; Kelvin Guu; Yonatan Oren; Percy Liang (NeurIPS, 2018)

Inferring Multi-Dimensional Rates of Aging from Cross-Sectional Data. - Emma Pierson; Pang Wei Koh; Tatsunori B. Hashimoto; Daphne Koller; Jure Leskovec; Nicholas Eriksson; Percy Liang (CoRR, 2018)

Generating Sentences by Editing Prototypes. - Kelvin Guu; Tatsunori B. Hashimoto; Yonatan Oren; Percy Liang (Trans. Assoc. Comput. Linguistics, 2018)

Inferring Multidimensional Rates of Aging from Cross-Sectional Data. - Emma Pierson; Pang Wei Koh; Tatsunori B. Hashimoto; Daphne Koller; Jure Leskovec; Nick Eriksson; Percy Liang (AISTATS, 2019)

Distributionally Robust Language Modeling. - Yonatan Oren; Shiori Sagawa; Tatsunori B. Hashimoto; Percy Liang (EMNLP/IJCNLP (1), 2019)

Unifying Human and Statistical Evaluation for Natural Language Generation. - Tatsunori B. Hashimoto; Hugh Zhang; Percy Liang (NAACL-HLT (1), 2019)

Learning Autocomplete Systems as a Communication Game. - Mina Lee 002; Tatsunori B. Hashimoto; Percy Liang (CoRR, 2019)

Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization. - Shiori Sagawa; Pang Wei Koh; Tatsunori B. Hashimoto; Percy Liang (CoRR, 2019)

Improved Natural Language Generation via Loss Truncation. - Daniel Kang; Tatsunori Hashimoto (ACL, 2020)

Distributionally Robust Neural Networks. - Shiori Sagawa; Pang Wei Koh; Tatsunori B. Hashimoto; Percy Liang (ICLR, 2020)

Robustness to Spurious Correlations via Human Annotations. - Megha Srivastava; Tatsunori B. Hashimoto; Percy Liang (ICML, 2020)

Task-agnostic Indexes for Deep Learning-based Queries over Unstructured Data. - Daniel Kang; John Guibas; Peter Bailis; Tatsunori Hashimoto; Matei Zaharia (CoRR, 2020)

Approximate Selection with Guarantees using Proxies. - Daniel Kang; Edward Gan; Peter Bailis; Tatsunori Hashimoto; Matei Zaharia (Proc. VLDB Endow., 2020)

Measuring Conversational Uptake: A Case Study on Student-Teacher Interactions. - Dorottya Demszky; Jing Liu 064; Zid Mancenido; Julie Cohen; Heather Hill; Dan Jurafsky; Tatsunori Hashimoto (ACL/IJCNLP (1), 2021)

The Disagreement Deconvolution: Bringing Machine Learning Performance Metrics In Line With Reality. - Mitchell L. Gordon; Kaitlyn Zhou; Kayur Patel; Tatsunori Hashimoto; Michael S. Bernstein (CHI, 2021)

Don't Hate the Player, Hate the Game: Safety and Utility in Multi-Agent Congestion Control. - Pratiksha Thaker; Matei Zaharia; Tatsunori Hashimoto (HotNets, 2021)

Modeling the Second Player in Distributionally Robust Optimization. - Paul Michel; Tatsunori Hashimoto; Graham Neubig (ICLR, 2021)

Model Performance Scaling with Multiple Data Sources. - Tatsunori Hashimoto (ICML, 2021)

DReCa: A General Task Augmentation Strategy for Few-Shot Natural Language Inference. - Shikhar Murty; Tatsunori Hashimoto; Christopher D. Manning (NAACL-HLT, 2021)

On the Inductive Bias of Masked Language Modeling: From Statistical to Syntactic Dependencies. - Tianyi Zhang; Tatsunori Hashimoto (NAACL-HLT, 2021)

The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics. - Sebastian Gehrmann; Tosin P. Adewumi; Karmanya Aggarwal; Pawan Sasanka Ammanamanchi; Aremu Anuoluwapo; Antoine Bosselut; Khyathi Raghavi Chandu; Miruna-Adriana Clinciu; Dipanjan Das 001; Kaustubh D. Dhole; Wanyu Du; Esin Durmus; et al. (CoRR, 2021)

Proof: Accelerating Approximate Aggregation Queries with Expensive Predicates. - Daniel Kang; John Guibas; Peter Bailis; Tatsunori Hashimoto; Yi Sun 010; Matei Zaharia (CoRR, 2021)

On the Opportunities and Risks of Foundation Models. - Rishi Bommasani; Drew A. Hudson; Ehsan Adeli 001; Russ B. Altman; Simran Arora; Sydney von Arx; Michael S. Bernstein; Jeannette Bohg; Antoine Bosselut; Emma Brunskill; Erik Brynjolfsson; Shyamal Buch; et al. (CoRR, 2021)

Accelerating Approximate Aggregation Queries with Expensive Predicates. - Daniel Kang; John Guibas; Peter Bailis; Tatsunori Hashimoto; Yi Sun 010; Matei Zaharia (Proc. VLDB Endow., 2021)

Spurious Correlations in Reference-Free Evaluation of Text Generation. - Esin Durmus; Faisal Ladhak; Tatsunori Hashimoto (ACL (1), 2022)

Jury Learning: Integrating Dissenting Voices into Machine Learning Models. - Mitchell L. Gordon; Michelle S. Lam; Joon Sung Park; Kayur Patel; Jeffrey T. Hancock; Tatsunori Hashimoto; Michael S. Bernstein (CHI, 2022)

Large Language Models Can Be Strong Differentially Private Learners. - Xuechen Li 005; Florian Tramèr; Percy Liang; Tatsunori Hashimoto (ICLR, 2022)

Distributionally Robust Models with Parametric Likelihood Ratios. - Paul Michel; Tatsunori Hashimoto; Graham Neubig (ICLR, 2022)

Extending the WILDS Benchmark for Unsupervised Adaptation. - Shiori Sagawa; Pang Wei Koh; Tony Lee; Irena Gao; Sang Michael Xie; Kendrick Shen; Ananya Kumar; Weihua Hu; Michihiro Yasunaga; Henrik Marklund; Sara Beery; Etienne David; et al. (ICLR, 2022)

Is Importance Weighting Incompatible with Interpolating Classifiers? - Ke Alexander Wang; Niladri Shekhar Chatterji; Saminul Haque; Tatsunori Hashimoto (ICLR, 2022)

Language modeling via stochastic processes. - Rose E. Wang; Esin Durmus; Noah D. Goodman; Tatsunori Hashimoto (ICLR, 2022)

Identifiability Conditions for Domain Adaptation. - Ishaan Gulrajani; Tatsunori Hashimoto (ICML, 2022)

Improving Self-Supervised Learning by Characterizing Idealized Representations. - Yann Dubois; Stefano Ermon; Tatsunori B. Hashimoto; Percy Liang (NeurIPS, 2022)

When Does Differentially Private Learning Not Suffer in High Dimensions? - Xuechen Li 005; Daogao Liu; Tatsunori B. Hashimoto; Huseyin A. Inan; Janardhan Kulkarni; Yin Tat Lee; Abhradeep Guha Thakurta (NeurIPS, 2022)

Diffusion-LM Improves Controllable Text Generation. - Xiang Lisa Li; John Thickstun; Ishaan Gulrajani; Percy Liang; Tatsunori B. Hashimoto (NeurIPS, 2022)

Factored DRO: Factored Distributionally Robust Policies for Contextual Bandits. - Tong Mu; Yash Chandak; Tatsunori B. Hashimoto; Emma Brunskill (NeurIPS, 2022)

TASTI: Semantic Indexes for Machine Learning-based Queries over Unstructured Data. - Daniel Kang; John Guibas; Peter D. Bailis; Tatsunori Hashimoto; Matei Zaharia (SIGMOD Conference, 2022)

TempLM: Distilling Language Models into Template-Based Generators. - Tianyi Zhang; Mina Lee 002; Lisa Li; Ende Shen; Tatsunori B. Hashimoto (CoRR, 2022)

Is a Caption Worth a Thousand Images? A Controlled Study for Representation Learning. - Shibani Santurkar; Yann Dubois; Rohan Taori; Percy Liang; Tatsunori Hashimoto (CoRR, 2022)

A Closer Look at the Calibration of Differentially Private Learners. - Hanlin Zhang 002; Xuechen Li 005; Prithviraj Sen; Salim Roukos; Tatsunori Hashimoto (CoRR, 2022)

Scaling up Trustless DNN Inference with Zero-Knowledge Proofs. - Daniel Kang; Tatsunori Hashimoto; Ion Stoica; Yi Sun 010 (CoRR, 2022)

ZK-IMG: Attested Images via Zero-Knowledge Proofs to Fight Disinformation. - Daniel Kang; Tatsunori Hashimoto; Ion Stoica; Yi Sun 010 (CoRR, 2022)

Tracing and Removing Data Errors in Natural Language Generation Datasets. - Faisal Ladhak; Esin Durmus; Tatsunori Hashimoto (CoRR, 2022)

Emergent Abilities of Large Language Models. - Jason Wei; Yi Tay; Rishi Bommasani; Colin Raffel; Barret Zoph; Sebastian Borgeaud; Dani Yogatama; Maarten Bosma; Denny Zhou; Donald Metzler; Ed H. Chi; Tatsunori Hashimoto; et al. (Trans. Mach. Learn. Res., 2022)

Contrastive Error Attribution for Finetuned Language Models. - Faisal Ladhak; Esin Durmus; Tatsunori Hashimoto (ACL (1), 2023)

Contrastive Decoding: Open-ended Text Generation as Optimization. - Xiang Lisa Li; Ari Holtzman; Daniel Fried; Percy Liang; Jason Eisner; Tatsunori Hashimoto; Luke Zettlemoyer; Mike Lewis (ACL (1), 2023)

Privacy-Preserving Domain Adaptation of Semantic Parsers. - Fatemehsadat Mireshghallah; Yu Su 001; Tatsunori Hashimoto; Jason Eisner; Richard Shin (ACL (1), 2023)

TempLM: Distilling Language Models into Template-Based Generators. - Tianyi Zhang; Mina Lee 002; Xiang Lisa Li; Ende Shen; Tatsunori Hashimoto (ACL (Findings), 2023)

When Do Pre-Training Biases Propagate to Downstream Tasks? A Case Study in Text Summarization. - Faisal Ladhak; Esin Durmus; Mirac Suzgun; Tianyi Zhang; Dan Jurafsky; Kathleen R. McKeown; Tatsunori Hashimoto (EACL, 2023)

Navigating the Grey Area: How Expressions of Uncertainty and Overconfidence Affect Language Models. - Kaitlyn Zhou; Dan Jurafsky; Tatsunori Hashimoto (EMNLP, 2023)

Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale. - Federico Bianchi 001; Pratyusha Kalluri; Esin Durmus; Faisal Ladhak; Myra Cheng; Debora Nozza; Tatsunori Hashimoto; Dan Jurafsky; James Zou 001; Aylin Caliskan (FAccT, 2023)

Is a Caption Worth a Thousand Images? A Study on Representation Learning. - Shibani Santurkar; Yann Dubois; Rohan Taori; Percy Liang; Tatsunori Hashimoto (ICLR, 2023)

Evaluating Self-Supervised Learning via Risk Decomposition. - Yann Dubois; Tatsunori Hashimoto; Percy Liang (ICML, 2023)

Out-of-Domain Robustness via Targeted Augmentations. - Irena Gao; Shiori Sagawa; Pang Wei Koh; Tatsunori Hashimoto; Percy Liang (ICML, 2023)

Whose Opinions Do Language Models Reflect? - Shibani Santurkar; Esin Durmus; Faisal Ladhak; Cinoo Lee; Percy Liang; Tatsunori Hashimoto (ICML, 2023)

Data Feedback Loops: Model-driven Amplification of Dataset Biases. - Rohan Taori; Tatsunori Hashimoto (ICML, 2023)

Coder Reviewer Reranking for Code Generation. - Tianyi Zhang; Tao Yu 009; Tatsunori Hashimoto; Mike Lewis; Wen-Tau Yih; Daniel Fried; Sida Wang 001 (ICML, 2023)

Congestion Control Safety via Comparative Statics. - Pratiksha Thaker; Matei Zaharia; Tatsunori Hashimoto (INFOCOM, 2023)

AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback. - Yann Dubois; Chen Xuechen Li; Rohan Taori; Tianyi Zhang; Ishaan Gulrajani; Jimmy Ba; Carlos Guestrin; Percy Liang; Tatsunori B. Hashimoto (NeurIPS, 2023)

Likelihood-Based Diffusion Language Models. - Ishaan Gulrajani; Tatsunori B. Hashimoto (NeurIPS, 2023)

MoCa: Measuring Human-Language Model Alignment on Causal and Moral Judgment Tasks. - Allen Nie; Yuhui Zhang; Atharva Amdekar; Chris Piech; Tatsunori B. Hashimoto; Tobias Gerstenberg (NeurIPS, 2023)

Navigating the Grey Area: Expressions of Overconfidence and Uncertainty in Language Models. - Kaitlyn Zhou; Dan Jurafsky; Tatsunori Hashimoto (CoRR, 2023)

AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback. - Yann Dubois; Xuechen Li 005; Rohan Taori; Tianyi Zhang; Ishaan Gulrajani; Jimmy Ba; Carlos Guestrin; Percy Liang; Tatsunori B. Hashimoto (CoRR, 2023)

Where's the Liability in Harmful AI Speech? - Peter Henderson 002; Tatsunori Hashimoto; Mark A. Lemley (CoRR, 2023)

Identifying and Mitigating the Security Risks of Generative AI. - Clark W. Barrett; Brad Boyd; Ellie Burzstein; Nicholas Carlini; Brad Chen; Jihye Choi; Amrita Roy Chowdhury 001; Mihai Christodorescu; Anupam Datta; Soheil Feizi; Kathleen Fisher; Tatsunori Hashimoto; et al. (CoRR, 2023)

Learning to (Learn at Test Time). - Yu Sun 020; Xinhao Li; Karan Dalal; Chloe Hsu; Sanmi Koyejo; Carlos Guestrin; Xiaolong Wang 004; Tatsunori Hashimoto; Xinlei Chen (CoRR, 2023)

Benchmarking Multi-Domain Active Learning on Image Classification. - Jiayi Li; Rohan Taori; Tatsunori B. Hashimoto (CoRR, 2023)

Identifying and Mitigating the Security Risks of Generative AI. - Clark W. Barrett; Brad Boyd; Elie Bursztein; Nicholas Carlini; Brad Chen; Jihye Choi; Amrita Roy Chowdhury 001; Mihai Christodorescu; Anupam Datta; Soheil Feizi; Kathleen Fisher; Tatsunori Hashimoto; et al. (Found. Trends Priv. Secur., 2023)

Foundation Models and Fair Use. - Peter Henderson 002; Xuechen Li 005; Dan Jurafsky; Tatsunori Hashimoto; Mark A. Lemley; Percy Liang (J. Mach. Learn. Res., 2023)

Accelerating Aggregation Queries on Unstructured Streams of Data. - Matthew Russo; Tatsunori Hashimoto; Daniel Kang; Yi Sun 010; Matei Zaharia (Proc. VLDB Endow., 2023)

Undersampling is a Minimax Optimal Robustness Intervention in Nonparametric Classification. - Niladri S. Chatterji; Saminul Haque; Tatsunori Hashimoto (Trans. Mach. Learn. Res., 2023)

Holistic Evaluation of Language Models. - Percy Liang; Rishi Bommasani; Tony Lee; Dimitris Tsipras; Dilara Soylu; Michihiro Yasunaga; Yian Zhang; Deepak Narayanan; Yuhuai Wu; Ananya Kumar; Benjamin Newman; Binhang Yuan; et al. (Trans. Mach. Learn. Res., 2023)

Distributionally Robust Losses for Latent Covariate Mixtures. - John C. Duchi; Tatsunori Hashimoto; Hongseok Namkoong (Oper. Res., 2023)

Safety-Tuned LLaMAs: Lessons From Improving the Safety of Large Language Models that Follow Instructions. - Federico Bianchi 001; Mirac Suzgun; Giuseppe Attanasio; Paul Röttger; Dan Jurafsky; Tatsunori Hashimoto; James Zou 001 (ICLR, 2024)

On the Fairness ROAD: Robust Optimization for Adversarial Debiasing. - Vincent Grari; Thibault Laugel; Tatsunori Hashimoto; Sylvain Lamprier; Marcin Detyniecki (ICLR, 2024)

On the Learnability of Watermarks for Language Models. - Chenchen Gu; Xiang Lisa Li; Percy Liang; Tatsunori Hashimoto (ICLR, 2024)

Benchmarking and Improving Generator-Validator Consistency of Language Models. - Xiang Lisa Li; Vaishnavi Shrivastava; Siyan Li; Tatsunori Hashimoto; Percy Liang (ICLR, 2024)

One Step of Gradient Descent is Provably the Optimal In-Context Learner with One Layer of Linear Self-Attention. - Arvind V. Mahankali; Tatsunori Hashimoto; Tengyu Ma 001 (ICLR, 2024)

Proving Test Set Contamination in Black-Box Language Models. - Yonatan Oren; Nicole Meister; Niladri S. Chatterji; Faisal Ladhak; Tatsunori Hashimoto (ICLR, 2024)

Identifying the Risks of LM Agents with an LM-Emulated Sandbox. - Yangjun Ruan; Honghua Dong; Andrew Wang; Silviu Pitis; Yongchao Zhou; Jimmy Ba; Yann Dubois; Chris J. Maddison; Tatsunori Hashimoto (ICLR, 2024)

Linguistic Calibration of Long-Form Generations. - Neil Band; Xuechen Li 005; Tengyu Ma 001; Tatsunori Hashimoto (ICML, 2024)

Scaling Laws for the Value of Individual Data Points in Machine Learning. - Ian Connick Covert; Wenlong Ji; Tatsunori Hashimoto; James Zou 001 (ICML, 2024)

Understanding Finetuning for Factual Knowledge Extraction. - Gaurav Rohit Ghosal; Tatsunori Hashimoto; Aditi Raghunathan (ICML, 2024)

Language Models with Conformal Factuality Guarantees. - Christopher Mohri; Tatsunori Hashimoto (ICML, 2024)

Trustless Audits without Revealing Data or Models. - Suppakit Waiwitlikhit; Ion Stoica; Yi Sun 010; Tatsunori Hashimoto; Daniel Kang (ICML, 2024)

Removing RLHF Protections in GPT-4 via Fine-Tuning. - Qiusi Zhan; Richard Fang; Rohan Bindu; Akul Gupta; Tatsunori Hashimoto; Daniel Kang (NAACL (Short Papers), 2024)

Stochastic Amortization: A Unified Approach to Accelerate Feature and Data Attribution. - Ian Covert; Chanwoo Kim 002; Su-In Lee; James Y. Zou; Tatsunori B. Hashimoto (NeurIPS, 2024)

Graph-based Uncertainty Metrics for Long-form Language Model Generations. - Mingjian Jiang; Yangjun Ruan; Prasanna Sattigeri; Salim Roukos; Tatsunori B. Hashimoto (NeurIPS, 2024)

Observational Scaling Laws and the Predictability of Langauge Model Performance. - Yangjun Ruan; Chris J. Maddison; Tatsunori B. Hashimoto (NeurIPS, 2024)

Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attacks. - Daniel Kang; Xuechen Li 005; Ion Stoica; Carlos Guestrin; Matei Zaharia; Tatsunori Hashimoto (SP (Workshops), 2024)

Linguistic Calibration of Language Models. - Neil Band; Xuechen Li 005; Tengyu Ma 001; Tatsunori Hashimoto (CoRR, 2024)

Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators. - Yann Dubois; Balázs Galambosi; Percy Liang; Tatsunori B. Hashimoto (CoRR, 2024)

Observational Scaling Laws and the Predictability of Language Model Performance. - Yangjun Ruan; Chris J. Maddison; Tatsunori Hashimoto (CoRR, 2024)

Learning to (Learn at Test Time): RNNs with Expressive Hidden States. - Yu Sun 020; Xinhao Li; Karan Dalal; Jiarui Xu; Arjun Vikram; Genghan Zhang; Yann Dubois; Xinlei Chen; Xiaolong Wang 004; Sanmi Koyejo; Tatsunori Hashimoto; Carlos Guestrin (CoRR, 2024)

AutoBencher: Creating Salient, Novel, Difficult Datasets for Language Models. - Xiang Lisa Li; Evan Zheran Liu; Percy Liang; Tatsunori Hashimoto (CoRR, 2024)

The Future of Open Human Feedback. - Shachar Don-Yehiya; Ben Burtenshaw; Ramón Fernandez Astudillo; Cailean Osborne; Mimansa Jaiswal; Tzu-Sheng Kuo; Wenting Zhao; Idan Shenfeld; Andi Peng; Mikhail Yurochkin; Atoosa Kasirzadeh; Yangsibo Huang; et al. (CoRR, 2024)

Graph-based Uncertainty Metrics for Long-form Language Model Outputs. - Mingjian Jiang; Yangjun Ruan; Prasanna Sattigeri; Salim Roukos; Tatsunori Hashimoto (CoRR, 2024)

The Extractive-Abstractive Spectrum: Uncovering Verifiability Trade-offs in LLM Generations. - Theodora Worledge; Tatsunori Hashimoto; Carlos Guestrin (CoRR, 2024)

Benchmarking Large Language Models for News Summarization. - Tianyi Zhang; Faisal Ladhak; Esin Durmus; Percy Liang; Kathleen R. McKeown; Tatsunori B. Hashimoto (Trans. Assoc. Comput. Linguistics, 2024)

A Survey on Data Selection for Language Models. - Alon Albalak; Yanai Elazar; Sang Michael Xie; Shayne Longpre; Nathan Lambert 001; Xinyi Wang 003; Niklas Muennighoff; Bairu Hou; Liangming Pan; Haewon Jeong; Colin Raffel; Shiyu Chang; et al. (Trans. Mach. Learn. Res., 2024)

Robust Distortion-free Watermarks for Language Models. - Rohith Kuditipudi; John Thickstun; Tatsunori Hashimoto; Percy Liang (Trans. Mach. Learn. Res., 2024)

Locality Alignment Improves Vision-Language Models. - Ian Connick Covert; Tony Sun; James Zou 001; Tatsunori Hashimoto (ICLR, 2025)

AutoBencher: Towards Declarative Benchmark Construction. - Xiang Lisa Li; Farzaan Kaiyom; Evan Zheran Liu; Yifan Mai 001; Percy Liang; Tatsunori Hashimoto (ICLR, 2025)

Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers. - Chenglei Si; Diyi Yang; Tatsunori Hashimoto (ICLR, 2025)

Improving Pretraining Data Using Perplexity Correlations. - Tristan Thrush; Christopher Potts; Tatsunori Hashimoto (ICLR, 2025)

Synthetic continued pretraining. - Zitong Yang; Neil Band; Shuangping Li; Emmanuel J. Candès; Tatsunori Hashimoto (ICLR, 2025)

Benchmarking Distributional Alignment of Large Language Models. - Nicole Meister; Carlos Guestrin; Tatsunori Hashimoto (NAACL (Long Papers), 2025)

s1: Simple test-time scaling. - Niklas Muennighoff; Zitong Yang; Weijia Shi; Xiang Lisa Li; Li Fei-Fei 001; Hannaneh Hajishirzi; Luke Zettlemoyer; Percy Liang; Emmanuel J. Candès; Tatsunori Hashimoto (CoRR, 2025)

Eliciting Language Model Behaviors with Investigator Agents. - Xiang Lisa Li; Neil Chowdhury; Daniel D. Johnson 001; Tatsunori Hashimoto; Percy Liang; Sarah Schwettmann; Jacob Steinhardt (CoRR, 2025)

CTC-DRO: Robust Optimization for Reducing Language Disparities in Speech Recognition. - Martijn Bartelds; Ananjan Nandi; Moussa Koulako Bala Doumbouya; Dan Jurafsky; Tatsunori Hashimoto; Karen Livescu (CoRR, 2025)

Auditing Prompt Caching in Language Model APIs. - Chenchen Gu; Xiang Lisa Li; Rohith Kuditipudi; Percy Liang; Tatsunori Hashimoto (CoRR, 2025)

FSPO: Few-Shot Preference Optimization of Synthetic Preference Data in LLMs Elicits Effective Personalization to Real Users. - Anikait Singh; Sheryl Hsu; Kyle Hsu; Eric Mitchell; Stefano Ermon; Tatsunori Hashimoto; Archit Sharma; Chelsea Finn (CoRR, 2025)

Reasoning to Learn from Latent Thoughts. - Yangjun Ruan; Neil Band; Chris J. Maddison; Tatsunori Hashimoto (CoRR, 2025)

One-Minute Video Generation with Test-Time Training. - Karan Dalal; Daniel Koceja; Gashon Hussein; Jiarui Xu; Yue Zhao 006; Youjin Song; Shihao Han; Ka Chun Cheung; Jan Kautz; Carlos Guestrin; Tatsunori Hashimoto; Sanmi Koyejo; et al. (CoRR, 2025)

LongCodeBench: Evaluating Coding LLMs at 1M Context Windows. - Stefano Rando; Luca Romani; Alessio Sampieri; Yuta Kyuragi; Luca Franco; Fabio Galasso; Tatsunori Hashimoto; John Yang 002 (CoRR, 2025)

Putting It All into Context: Simplifying Agents with LCLMs. - Mingjian Jiang; Yangjun Ruan; Luis A. Lastras; Pavan Kapanipathi; Tatsunori Hashimoto (CoRR, 2025)

OpenThoughts: Data Recipes for Reasoning Models. - Etash Kumar Guha; Ryan Marten; Sedrick Keh; Negin Raoof; Georgios Smyrnis; Hritik Bansal; Marianna Nezhurina; Jean Mercat; Trung Vu 002; Zayne Sprague; Ashima Suvarna; Benjamin Feuer; et al. (CoRR, 2025)

The Ideation-Execution Gap: Execution Outcomes of LLM-Generated versus Human Research Ideas. - Chenglei Si; Tatsunori Hashimoto; Diyi Yang (CoRR, 2025)