1.2k citations · 2.4k across the 25 of their papers we have counts for
23 papers · 1 filter
string2string Studio: An Interactive, In-Browser Platform for String-to-String Algorithms
Mirac Suzgun, James Zou, Stuart M. Shieber +1
We present string2string Studio, an interactive in-browser platform for string-to-string analysis across natural language processing, computational biology, and the digital humanit…
Evaluating Commercial AI Chatbots as News Intermediaries
Mirac Suzgun, Emily Shen, Federico Bianchi +5
AI chatbots are rapidly shaping how people encounter the news, yet no prior study has systematically measured how accurately these systems, with their proprietary search integratio…
AI for Scaling Legal Reform: Mapping and Redacting Racial Covenants in Santa Clara County
Faiz Surani, Mirac Suzgun, Vyoma Raman +3
Legal reform can be challenging in light of the volume, complexity, and interdependence of laws, codes, and records. One salient example of this challenge is the effort to restrict…
Belief in the Machine: Investigating Epistemological Blind Spots of Language Models
Mirac Suzgun, Tayfun Gur, Federico Bianchi +4
As language models (LMs) become integral to fields like healthcare, law, and journalism, their ability to differentiate between fact, belief, and knowledge is essential for reliabl…
Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools
Varun Magesh, Faiz Surani, Matthew Dahl +3
Legal practice has witnessed a sharp rise in products incorporating artificial intelligence (AI). Such tools are designed to assist with a wide range of core legal tasks, from sear…
When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards
Norah Alzahrani, Hisham Abdullah Alyahya, Yazeed Alnumay +9
Large Language Model (LLM) leaderboards based on benchmark rankings are regularly used to guide practitioners in model selection. Often, the published leaderboard rankings are take…
Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffolding
Mirac Suzgun, Adam Tauman Kalai
We introduce meta-prompting, an effective scaffolding technique designed to enhance the functionality of language models (LMs). This approach transforms a single LM into a multi-fa…
Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models
Matthew Dahl, Varun Magesh, Mirac Suzgun +1
Do large language models (LLMs) know the law? These models are increasingly being used to augment legal practice, education, and research, yet their revolutionary potential is thre…
A Benchmark for Learning to Translate a New Language from One Grammar Book
Garrett Tanzer, Mirac Suzgun, Eline Visser +2
Large language models (LLMs) can perform impressive feats with in-context learning or lightweight finetuning. It is natural to wonder how well these models adapt to genuinely new t…
Safety-Tuned LLaMAs: Lessons From Improving the Safety of Large Language Models that Follow Instructions
Federico Bianchi, Mirac Suzgun, Giuseppe Attanasio +4
Training large language models to follow instructions makes them perform better on a wide range of tasks and generally become more helpful. However, a perfectly helpful model will…
Do Language Models Know When They're Hallucinating References?
Ayush Agrawal, Mirac Suzgun, Lester Mackey +1
State-of-the-art language models (LMs) are notoriously susceptible to generating hallucinated information. Such inaccurate outputs not only undermine the reliability of these model…
string2string: A Modern Python Library for String-to-String Algorithms
Mirac Suzgun, Stuart M. Shieber, Dan Jurafsky
We introduce string2string, an open-source library that offers a comprehensive suite of efficient algorithms for a broad range of string-to-string problems. It includes traditional…
Follow the Wisdom of the Crowd: Effective Text Generation via Minimum Bayes Risk Decoding
Mirac Suzgun, Luke Melas-Kyriazi, Dan Jurafsky
In open-ended natural-language generation, existing text decoding methods typically struggle to produce text which is both diverse and high-quality. Greedy and beam search are know…
Holistic Evaluation of Language Models
Percy Liang, Rishi Bommasani, Tony Lee +47
Language models (LMs) are becoming the foundation for almost all major language technologies, but their capabilities, limitations, and risks are not well understood. We present Hol…
Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them
Mirac Suzgun, Nathan Scales, Nathanael Schärli +8
BIG-Bench (Srivastava et al., 2022) is a diverse evaluation suite that focuses on tasks believed to be beyond the capabilities of current language models. Language models have alre…
Language Models are Multilingual Chain-of-Thought Reasoners
Freda Shi, Mirac Suzgun, Markus Freitag +9
We evaluate the reasoning abilities of large language models in multilingual settings. We introduce the Multilingual Grade School Math (MGSM) benchmark, by manually translating 250…
The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications
Mirac Suzgun, Luke Melas-Kyriazi, Suproteem K. Sarkar +2
Innovation is a major driver of economic and social development, and information about many kinds of innovation is embedded in semi-structured data from patents and patent applicat…
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao +448
Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabil…
Prompt-and-Rerank: A Method for Zero-Shot and Few-Shot Arbitrary Textual Style Transfer with Small Language Models
Mirac Suzgun, Luke Melas-Kyriazi, Dan Jurafsky
We propose a method for arbitrary textual style transfer (TST)--the task of transforming a text into any given style--utilizing general-purpose pre-trained language models. Our met…
Monte Carlo Tree Search for Interpreting Stress in Natural Language
Kyle Swanson, Joy Hsu, Mirac Suzgun
Natural language processing can facilitate the analysis of a person's mental state from text they have written. Previous studies have developed models that can predict whether a pe…
Memory-Augmented Recurrent Neural Networks Can Learn Generalized Dyck Languages
Mirac Suzgun, Sebastian Gehrmann, Yonatan Belinkov +1
We introduce three memory-augmented Recurrent Neural Networks (MARNNs) and explore their capabilities on a series of simple language modeling tasks whose solutions require stack-ba…
LSTM Networks Can Perform Dynamic Counting
Mirac Suzgun, Sebastian Gehrmann, Yonatan Belinkov +1
In this paper, we systematically assess the ability of standard recurrent networks to perform dynamic counting and to encode hierarchical representations. All the neural models in…
On Evaluating the Generalization of LSTM Models in Formal Languages
Mirac Suzgun, Yonatan Belinkov, Stuart M. Shieber
Recurrent Neural Networks (RNNs) are theoretically Turing-complete and established themselves as a dominant model for language processing. Yet, there still remains an uncertainty r…