activity
20212026
most citedPhi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

163 citations · 181 across the 14 of their papers we have counts for

collaborators

14 papers

cs.CL2026

CAPO: Constraint-Aware Prompt Optimization for LLM Agents

Victor Ye Dong, Reid Pryzant, Yi Liu +1

Large language models (LLMs) are increasingly deployed as agents that rely on system prompts to use tools and complete tasks. Such deployments impose distinct operational requireme…

cs.CL2025

The Bias is in the Details: An Assessment of Cognitive Bias in LLMs

R. Alexander Knipper, Charles S. Knipper, Kaiqi Zhang +3

As Large Language Models (LLMs) are increasingly embedded in real-world decision-making processes, it becomes crucial to examine the extent to which they exhibit cognitive biases.…

cs.CL2024★ 163 cited

Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Marah Abdin, Jyoti Aneja, Hany Awadalla +126

We introduce phi-3-mini, a 3.8 billion parameter language model trained on 3.3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal test…

cs.CL2023★ 1 cited

The Shifted and The Overlooked: A Task-oriented Investigation of User-GPT Interactions

Siru Ouyang, Shuohang Wang, Yang Liu +7

Recent progress in Large Language Models (LLMs) has produced models that exhibit remarkable performance across a variety of NLP tasks. However, it remains unclear whether the exist…

cs.LG2023★ 2 cited

Soft Convex Quantization: Revisiting Vector Quantization with Convex Optimization

Tanmay Gautam, Reid Pryzant, Ziyi Yang +2

Vector Quantization (VQ) is a well-known technique in deep learning for extracting informative discrete latent representations. VQ-embedded models have shown impressive results in…

cs.CL2023★ 2 cited

i-Code Studio: A Configurable and Composable Framework for Integrative AI

Yuwei Fang, Mahmoud Khademi, Chenguang Zhu +8

Artificial General Intelligence (AGI) requires comprehensive understanding and generation capabilities for a variety of tasks spanning different modalities and functionalities. Int…

cs.CL2023★ 1 cited

i-Code V2: An Autoregressive Generation Framework over Vision, Language, and Speech Data

Ziyi Yang, Mahmoud Khademi, Yichong Xu +16

The convergence of text, visual, and audio data is a key step towards human-like artificial intelligence, however the current Vision-Language-Speech landscape is dominated by encod…

cs.CL2023

In-Context Demonstration Selection with Cross Entropy Difference

Dan Iter, Reid Pryzant, Ruochen Xu +4

Large language models (LLMs) can use in-context demonstrations to improve performance on zero-shot tasks. However, selecting the best in-context examples is challenging because mod…

cs.CL2023★ 3 cited

Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reid Pryzant, Dan Iter, Jerry Li +3

Large Language Models (LLMs) have shown impressive performance as general purpose agents, but their abilities remain highly dependent on prompts which are hand written with onerous…

cs.CL2022★ 1 cited

APOLLO: A Simple Approach for Adaptive Pretraining of Language Models for Logical Reasoning

Soumya Sanyal, Yichong Xu, Shuohang Wang +5

Logical reasoning of text is an important ability that requires understanding the information present in the text, their interconnections, and then reasoning through them to infer…

cs.CL2022★ 2 cited

FAST: Improving Controllability for Text Generation with Feedback Aware Self-Training

Junyi Chai, Reid Pryzant, Victor Ye Dong +3

Controllable text generation systems often leverage control codes to direct various properties of the output like style and length. Inspired by recent work on causal inference for…

cs.LG2022★ 4 cited

i-Code: An Integrative and Composable Multimodal Learning Framework

Ziyi Yang, Yuwei Fang, Chenguang Zhu +17

Human intelligence is multimodal; we integrate visual, linguistic, and acoustic signals to maintain a holistic worldview. Most current pretraining methods, however, are limited to…

cs.CL2022★ 2 cited

Automatic Rule Induction for Interpretable Semi-Supervised Learning

Reid Pryzant, Ziyi Yang, Yichong Xu +2

Semi-supervised learning has shown promise in allowing NLP models to generalize from small amounts of labeled data. Meanwhile, pretrained transformer models act as black-box correl…

cs.CL2021

Repairing Pronouns in Translation with BERT-Based Post-Editing

Reid Pryzant

Pronouns are important determinants of a text's meaning but difficult to translate. This is because pronoun choice can depend on entities described in previous sentences, and in so…