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20212025
most citedEvaluating Large Language Models Trained on Code

1.5k citations · 2.1k across the 12 of their papers we have counts for

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9 papers · 1 filter

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

Considering Length Diversity in Retrieval-Augmented Summarization

Juseon-Do, Jaesung Hwang, Jingun Kwon +2

This study investigates retrieval-augmented summarization by specifically examining the impact of exemplar summary lengths under length constraints, not covered by previous work. W…

cs.CL2025

DebateBench: A Challenging Long Context Reasoning Benchmark For Large Language Models

Utkarsh Tiwari, Aryan Seth, Adi Mukherjee +3

We introduce DebateBench, a novel dataset consisting of an extensive collection of transcripts and metadata from some of the world's most prestigious competitive debates. The datas…

cs.CL2025

ARISE: Iterative Rule Induction and Synthetic Data Generation for Text Classification

Yashwanth M., Vaibhav Singh, Ayush Maheshwari +2

We propose ARISE, a framework that iteratively induces rules and generates synthetic data for text classification. We combine synthetic data generation and automatic rule induction…

cs.CL2024

CodeInsight: A Curated Dataset of Practical Coding Solutions from Stack Overflow

Nathanaël Beau, Benoît Crabbé

We introduce a novel dataset tailored for code generation, aimed at aiding developers in common tasks. Our dataset provides examples that include a clarified intent, code snippets…

cs.CL2024★ 3 cited

Rel-A.I.: An Interaction-Centered Approach To Measuring Human-LM Reliance

Kaitlyn Zhou, Jena D. Hwang, Xiang Ren +3

The ability to communicate uncertainty, risk, and limitation is crucial for the safety of large language models. However, current evaluations of these abilities rely on simple cali…

cs.CL2024

Losing Visual Needles in Image Haystacks: Vision Language Models are Easily Distracted in Short and Long Contexts

Aditya Sharma, Michael Saxon, William Yang Wang

We present LoCoVQA, a dynamic benchmark generator for evaluating long-context extractive reasoning in vision language models (VLMs). LoCoVQA augments test examples for mathematical…

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…

cs.CL2022★ 565 cited

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao +448

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabil…