25 citations · 30 across the 41 of their papers we have counts for
23 papers · 1 filter
SpecDiff-2: Scaling Diffusion Drafter Alignment For Faster Speculative Decoding
Jameson Sandler, Jacob K. Christopher, Thomas Hartvigsen +1
Speculative decoding has become the standard approach for accelerating Large Language Model (LLM) inference. It exploits a lossless draft-then-verify procedure to circumvent the la…
EDUMATH: Generating Standards-aligned Educational Math Word Problems
Bryan R. Christ, Penelope Molitz, Beau LeBlond +3
Math word problems (MWPs) are critical K-12 educational tools, and customizing them to students' interests and ability levels can enhance learning. However, teachers struggle to fi…
Decoding the Rule Book: Extracting Hidden Moderation Criteria from Reddit Communities
Youngwoo Kim, Himanshu Beniwal, Steven L. Johnson +1
Effective content moderation systems require explicit classification criteria, yet online communities like subreddits often operate with diverse, implicit standards. This work intr…
BEDTime: A Unified Benchmark for Automatically Describing Time Series
Medhasweta Sen, Zachary Gottesman, Jiaxing Qiu +3
Recent works propose complex multi-modal models that handle both time series and language, ultimately claiming high performance on complex tasks like time series reasoning and cros…
ADMIRE-BayesOpt: Accelerated Data MIxture RE-weighting for Language Models with Bayesian Optimization
Shengzhuang Chen, Xu Ouyang, Michael Arthur Leopold Pearce +2
Determining the optimal data mixture for large language model training remains a challenging problem with an outsized impact on performance. In practice, language model developers…
Instruction-based Time Series Editing
Jiaxing Qiu, Dongliang Guo, Brynne Sullivan +2
In time series editing, we aim to modify some properties of a given time series without altering others. For example, when analyzing a hospital patient's blood pressure, we may add…
ModelCitizens: Representing Community Voices in Online Safety
Ashima Suvarna, Christina Chance, Karolina Naranjo +4
Automatic toxic language detection is critical for creating safe, inclusive online spaces. However, it is a highly subjective task, with perceptions of toxic language shaped by com…
BalancEdit: Dynamically Balancing the Generality-Locality Trade-off in Multi-modal Model Editing
Dongliang Guo, Mengxuan Hu, Zihan Guan +2
Large multi-modal models inevitably decay over time as facts update and previously learned information becomes outdated. Traditional approaches such as fine-tuning are often imprac…
Efficient Knowledge Editing via Minimal Precomputation
Akshat Gupta, Maochuan Lu, Thomas Hartvigsen +1
Knowledge editing methods like MEMIT are able to make data and compute efficient updates of factual knowledge by using a single sentence to update facts and their consequences. How…
KScope: A Framework for Characterizing the Knowledge Status of Language Models
Yuxin Xiao, Shan Chen, Jack Gallifant +3
Characterizing a large language model's (LLM's) knowledge of a given question is challenging. As a result, prior work has primarily examined LLM behavior under knowledge conflicts,…
Continually Self-Improving Language Models for Bariatric Surgery Question--Answering
Yash Kumar Atri, Thomas H Shin, Thomas Hartvigsen
While bariatric and metabolic surgery (MBS) is considered the gold standard treatment for severe and morbid obesity, its therapeutic efficacy hinges upon active and longitudinal en…
MedBrowseComp: Benchmarking Medical Deep Research and Computer Use
Shan Chen, Pedro Moreira, Yuxin Xiao +6
Large language models (LLMs) are increasingly envisioned as decision-support tools in clinical practice, yet safe clinical reasoning demands integrating heterogeneous knowledge bas…
Model Editing with Graph-Based External Memory
Yash Kumar Atri, Ahmed Alaa, Thomas Hartvigsen
Large language models (LLMs) have revolutionized natural language processing, yet their practical utility is often limited by persistent issues of hallucinations and outdated param…
Breaking mBad! Supervised Fine-tuning for Cross-Lingual Detoxification
Himanshu Beniwal, Youngwoo Kim, Maarten Sap +2
As large language models (LLMs) become increasingly prevalent in global applications, ensuring that they are toxicity-free across diverse linguistic contexts remains a critical cha…
PolyGuard: A Multilingual Safety Moderation Tool for 17 Languages
Priyanshu Kumar, Devansh Jain, Akhila Yerukola +4
Truly multilingual safety moderation efforts for Large Language Models (LLMs) have been hindered by a narrow focus on a small set of languages (e.g., English, Chinese) as well as a…
Medical Large Language Model Benchmarks Should Prioritize Construct Validity
Ahmed Alaa, Thomas Hartvigsen, Niloufar Golchini +4
Medical large language models (LLMs) research often makes bold claims, from encoding clinical knowledge to reasoning like a physician. These claims are usually backed by evaluation…
Disparities in LLM Reasoning Accuracy and Explanations: A Case Study on African American English
Runtao Zhou, Guangya Wan, Saadia Gabriel +4
Large Language Models (LLMs) have demonstrated remarkable capabilities in reasoning tasks, leading to their widespread deployment. However, recent studies have highlighted concerni…
How Can Time Series Analysis Benefit From Multiple Modalities? A Survey and Outlook
Haoxin Liu, Harshavardhan Kamarthi, Zhiyuan Zhao +6
Time series analysis (TSA) is a longstanding research topic in the data mining community and has wide real-world significance. Compared to "richer" modalities such as language and…
Constrained Discrete Diffusion
Michael Cardei, Jacob K Christopher, Thomas Hartvigsen +2
Discrete diffusion models are a class of generative models that construct sequences by progressively denoising samples from a categorical noise distribution. Beyond their rapidly g…
Norm Growth and Stability Challenges in Localized Sequential Knowledge Editing
Akshat Gupta, Christine Fang, Atahan Ozdemir +4
This study investigates the impact of localized updates to large language models (LLMs), specifically in the context of knowledge editing - a task aimed at incorporating or modifyi…
Sparse Autoencoder Features for Classifications and Transferability
Jack Gallifant, Shan Chen, Kuleen Sasse +3
Sparse Autoencoders (SAEs) provide potentials for uncovering structured, human-interpretable representations in Large Language Models (LLMs), making them a crucial tool for transpa…
Lifelong Knowledge Editing requires Better Regularization
Akshat Gupta, Phudish Prateepamornkul, Maochuan Lu +3
Knowledge editing is a promising way to improve factuality in large language models, but recent studies have shown significant model degradation during sequential editing. In this…
Labeling Free-text Data using Language Model Ensembles
Jiaxing Qiu, Dongliang Guo, Natalie Papini +5
Free-text responses are commonly collected in psychological studies, providing rich qualitative insights that quantitative measures may not capture. Labeling curated topics of rese…