activity
20232026
most citedGPT-4o System Card

164 citations · 197 across the 12 of their papers we have counts for

collaborators

13 papers

cs.CL2026

The Dialect Tax: Dialectal Biases Persist throughout the Language Modeling Pipeline

Elle

Systematic dialectal performance gaps in language models (LMs) are well documented, but the source of these disparities within the modern language modeling pipeline remains unclear…

cs.CL2026

TLPO: Token-Level Policy Optimization for Mitigating Language Confusion in Large Language Models

Jinho Choo, JunSeung Lee, Jimyeong Kim +3

Large language models (LLMs) demonstrate strong multilingual capabilities, yet often fail to consistently generate responses in the intended language, exhibiting a phenomenon known…

cs.CL2026

Adam's Law: Textual Frequency Law on Large Language Models

Hongyuan Adam Lu, Z. L., Victor Wei +5

While textual frequency has been validated as relevant to human cognition in reading speed, its relatedness to Large Language Models (LLMs) is seldom studied. We propose a novel re…

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.LG2025

Motivating Next-Gen Accelerators with Flexible (N:M) Activation Sparsity via Benchmarking Lightweight Post-Training Sparsification Approaches

Shirin Alanova, Kristina Kazistova, Ekaterina Galaeva +7

The demand for efficient large language model (LLM) inference has intensified the focus on sparsification techniques. While semi-structured (N:M) pruning is well-established for we…

cs.CL2025

Balancing Knowledge Delivery and Emotional Comfort in Healthcare Conversational Systems

Shang-Chi Tsai, Yun-Nung Chen

With the advancement of large language models, many dialogue systems are now capable of providing reasonable and informative responses to patients' medical conditions. However, whe…

cs.CL2025★ 5 cited

FinanceReasoning: Benchmarking Financial Numerical Reasoning More Credible, Comprehensive and Challenging

Zichen Tang, Haihong E, Ziyan Ma +10

We introduce FinanceReasoning, a novel benchmark designed to evaluate the reasoning capabilities of large reasoning models (LRMs) in financial numerical reasoning problems. Compare…

cs.CL2025

Self-Error-Instruct: Generalizing from Errors for LLMs Mathematical Reasoning

Erxin Yu, Jing Li, Ming Liao +7

Although large language models demonstrate strong performance across various domains, they still struggle with numerous bad cases in mathematical reasoning. Previous approaches to…

cs.LG2025★ 6 cited

Competitive Programming with Large Reasoning Models

OpenAI, :, Ahmed El-Kishky +23

We show that reinforcement learning applied to large language models (LLMs) significantly boosts performance on complex coding and reasoning tasks. Additionally, we compare two gen…

cs.CL2024★ 164 cited

GPT-4o System Card

OpenAI, :, Aaron Hurst +416

GPT-4o is an autoregressive omni model that accepts as input any combination of text, audio, image, and video, and generates any combination of text, audio, and image outputs. It's…

cs.CL2024★ 2 cited

BEnQA: A Question Answering and Reasoning Benchmark for Bengali and English

Sheikh Shafayat, H M Quamran Hasan, Minhajur Rahman Chowdhury Mahim +3

In this study, we introduce BEnQA, a dataset comprising parallel Bengali and English exam questions for middle and high school levels in Bangladesh. Our dataset consists of approxi…

cs.CL2024★ 2 cited

LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression

Zhuoshi Pan, Qianhui Wu, Huiqiang Jiang +10

This paper focuses on task-agnostic prompt compression for better generalizability and efficiency. Considering the redundancy in natural language, existing approaches compress prom…

cs.CL2023

GPT-4 Technical Report

OpenAI, Josh Achiam, Steven Adler +278

We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-wor…