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20172026
most citedPhi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

163 citations · 375 across the 36 of their papers we have counts for

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7 papers · 1 filter

cs.CL2026★ 18 cited

OpenAI GPT-5 System Card

Aaditya Singh, Adam Fry, Adam Perelman +483

This is the system card published alongside the OpenAI GPT-5 launch, August 2025. GPT-5 is a unified system with a smart and fast model that answers most questions, a deeper reason…

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.CL2025

gpt-oss-120b & gpt-oss-20b Model Card

OpenAI, :, Sandhini Agarwal +124

We present gpt-oss-120b and gpt-oss-20b, two open-weight reasoning models that push the frontier of accuracy and inference cost. The models use an efficient mixture-of-expert trans…

cs.CL2025

On the Query Complexity of Verifier-Assisted Language Generation

Edoardo Botta, Yuchen Li, Aashay Mehta +3

Recently, a plethora of works have proposed inference-time algorithms (e.g. best-of-n), which incorporate verifiers to assist the generation process. Their quality-efficiency trade…

cs.CL2024★ 30 cited

Phi-4 Technical Report

Marah Abdin, Jyoti Aneja, Harkirat Behl +24

We present phi-4, a 14-billion parameter language model developed with a training recipe that is centrally focused on data quality. Unlike most language models, where pre-training…

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.CL2019★ 13 cited

Calibration, Entropy Rates, and Memory in Language Models

Mark Braverman, Xinyi Chen, Sham M. Kakade +3

Building accurate language models that capture meaningful long-term dependencies is a core challenge in natural language processing. Towards this end, we present a calibration-base…