8 citations · 10 across the 8 of their papers we have counts for
10 papers
Early Discoveries of Algorithmist I: Promise of Provable Algorithm Synthesis at Scale
Janardhan Kulkarni
Designing algorithms with provable guarantees that also work well in practice remains difficult, requiring both mathematical reasoning and careful implementation. Existing approach…
Simulating Environments with Reasoning Models for Agent Training
Yuetai Li, Huseyin A Inan, Xiang Yue +6
LLM agents excel in compact environments requiring deep reasoning but remain brittle when operating in broader, more complex contexts that demand robustness across diverse tools an…
Contextual Integrity in LLMs via Reasoning and Reinforcement Learning
Guangchen Lan, Huseyin A. Inan, Sahar Abdelnabi +5
As the era of autonomous agents making decisions on behalf of users unfolds, ensuring contextual integrity (CI) -- what is the appropriate information to share while carrying out a…
On the Emergence of Thinking in LLMs I: Searching for the Right Intuition
Guanghao Ye, Khiem Duc Pham, Xinzhi Zhang +5
Recent AI advancements, such as OpenAI's new models, are transforming LLMs into LRMs (Large Reasoning Models) that perform reasoning during inference, taking extra time and compute…
DiscQuant: A Quantization Method for Neural Networks Inspired by Discrepancy Theory
Jerry Chee, Arturs Backurs, Rainie Heck +4
Quantizing the weights of a neural network has two steps: (1) Finding a good low bit-complexity representation for weights (which we call the quantization grid) and (2) Rounding th…
Differentially Private Training of Mixture of Experts Models
Pierre Tholoniat, Huseyin A. Inan, Janardhan Kulkarni +1
This position paper investigates the integration of Differential Privacy (DP) in the training of Mixture of Experts (MoE) models within the field of natural language processing. As…
TinyGSM: achieving >80% on GSM8k with small language models
Bingbin Liu, Sebastien Bubeck, Ronen Eldan +5
Small-scale models offer various computational advantages, and yet to which extent size is critical for problem-solving abilities remains an open question. Specifically for solving…
Assessing Privacy Risks in Language Models: A Case Study on Summarization Tasks
Ruixiang Tang, Gord Lueck, Rodolfo Quispe +3
Large language models have revolutionized the field of NLP by achieving state-of-the-art performance on various tasks. However, there is a concern that these models may disclose in…
Privately Aligning Language Models with Reinforcement Learning
Fan Wu, Huseyin A. Inan, Arturs Backurs +3
Positioned between pre-training and user deployment, aligning large language models (LLMs) through reinforcement learning (RL) has emerged as a prevailing strategy for training ins…
Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation
Xinyu Tang, Richard Shin, Huseyin A. Inan +6
We study the problem of in-context learning (ICL) with large language models (LLMs) on private datasets. This scenario poses privacy risks, as LLMs may leak or regurgitate the priv…