activity
20192026
most citedSecure Computation for Machine Learning With SPDZ

44 citations · 104 across the 47 of their papers we have counts for

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Showing cs.CLShow all

7 papers · 1 filter

cs.CL2026

VibeJam: A User Study Platform for Web Development with Agents

Nishant Balepur, Connor Baumler, Valerie Chen +3

Programming with AI is increasingly agentic, users prompt LLMs to directly edit their code and review the changes, with adoption growing especially for web development tasks. Despi…

cs.CL2026

(Im)Paired Programming: Coding Agents Improve Productivity but Harm Understanding

Nishant Balepur, Connor Baumler, Valerie Chen +3

Coding agents (e.g., Cursor) improve developer productivity by optimizing task completion, but shifting users from writing code to prompting and reviewing may harm their understand…

cs.CL2026

ReproRepo: Scaling Reproducibility Audits with GitHub Repository Issues

Shanda Li, Qiuhong Anna Wei, Jingwu Tang +5

Reproducing research results from papers and released code is central to scientific progress. Existing works have introduced benchmarks to evaluate whether LLM agents can assist wi…

cs.CL2025

Completion Collaboration: Scaling Collaborative Effort with Agents

Shannon Zejiang Shen, Valerie Chen, Ken Gu +11

Current evaluations of agents remain centered around one-shot task completion, failing to account for the inherently iterative and collaborative nature of many real-world problems,…

cs.CL2025

Coding Agents with Multimodal Browsing are Generalist Problem Solvers

Aditya Bharat Soni, Boxuan Li, Xingyao Wang +2

Modern human labor is characterized by specialization; we train for years and develop particular tools that allow us to perform well across a variety of tasks. In addition, AI agen…

cs.CL2025

Measuring LLM Novelty As The Frontier Of Original And High-Quality Output

Vishakh Padmakumar, Chen Yueh-Han, Jane Pan +2

As large language models (LLMs) are increasingly used for ideation and scientific discovery, it is important to evaluate their ability to generate novel output. Prior work evaluate…

cs.CL2023★ 5 cited

Do LLMs exhibit human-like response biases? A case study in survey design

Lindia Tjuatja, Valerie Chen, Sherry Tongshuang Wu +2

As large language models (LLMs) become more capable, there is growing excitement about the possibility of using LLMs as proxies for humans in real-world tasks where subjective labe…