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20152026
most citedAsk Me Anything: Dynamic Memory Networks for Natural Language Processing

626 citations · 1.7k across the 87 of their papers we have counts for

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6 papers · 1 filter

cs.LG2026

Bio-Inspired Self-Supervised Learning for Wrist-worn Accelerometer Data

Prithviraj Tarale, Kiet Chu, Abhishek Varghese +4

Wearable accelerometers enable large-scale health monitoring, yet learning robust human-activity representations has been constrained by scarce labeled data. While self-supervised…

cs.LG2026

How2Everything: Mining the Web for How-To Procedures to Evaluate and Improve LLMs

Yapei Chang, Kyle Lo, Mohit Iyyer +1

Generating step-by-step "how-to" procedures is a key LLM capability: how-to advice is commonly requested in chatbots, and step-by-step planning is critical for reasoning over compl…

cs.LG2025

OverThink: Slowdown Attacks on Reasoning LLMs

Abhinav Kumar, Jaechul Roh, Ali Naseh +4

A reasoning language model (RLM) generates costly reasoning tokens, often hidden from the users, that help it excel at many tasks. Our Overthink attack targets RLM-based applicatio…

cs.LG2024

PostMark: A Robust Blackbox Watermark for Large Language Models

Yapei Chang, Kalpesh Krishna, Amir Houmansadr +2

The most effective techniques to detect LLM-generated text rely on inserting a detectable signature -- or watermark -- during the model's decoding process. Most existing watermarki…

cs.LG2023★ 15 cited

Stealing the Decoding Algorithms of Language Models

Ali Naseh, Kalpesh Krishna, Mohit Iyyer +1

A key component of generating text from modern language models (LM) is the selection and tuning of decoding algorithms. These algorithms determine how to generate text from the int…

cs.LG2019★ 3 cited

Casting Light on Invisible Cities: Computationally Engaging with Literary Criticism

Shufan Wang, Mohit Iyyer

Literary critics often attempt to uncover meaning in a single work of literature through careful reading and analysis. Applying natural language processing methods to aid in such l…