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20222026
most citedExploring the Benefits of Training Expert Language Models over Instruction Tuning

20 citations · 52 across the 9 of their papers we have counts for

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8 papers · 1 filter

cs.CL2026

SPADE: Self-Play in Adaptive Synthetic Executable Environments

Bo Liu, Simon Yu, Yiding Jiang +15

Continuous self-improvement requires an ever-expanding pool of self-generated, diverse, adaptive goals. For language agents, existing training environment pools (hand-curated, stat…

cs.CL202413 cited

Consent in Crisis: The Rapid Decline of the AI Data Commons

Shayne Longpre, Robert Mahari, Ariel Lee +46

General-purpose artificial intelligence (AI) systems are built on massive swathes of public web data, assembled into corpora such as C4, RefinedWeb, and Dolma. To our knowledge, we…

cs.CL20241 cited

Language Models as Compilers: Simulating Pseudocode Execution Improves Algorithmic Reasoning in Language Models

Hyungjoo Chae, Yeonghyeon Kim, Seungone Kim +8

Algorithmic reasoning refers to the ability to understand the complex patterns behind the problem and decompose them into a sequence of reasoning steps towards the solution. Such n…

cs.CL2024

Prometheus-Vision: Vision-Language Model as a Judge for Fine-Grained Evaluation

Seongyun Lee, Seungone Kim, Sue Hyun Park +2

Assessing long-form responses generated by Vision-Language Models (VLMs) is challenging. It not only requires checking whether the VLM follows the given instruction but also verify…

cs.CL20236 cited

Personalized Soups: Personalized Large Language Model Alignment via Post-hoc Parameter Merging

Joel Jang, Seungone Kim, Bill Yuchen Lin +6

While Reinforcement Learning from Human Feedback (RLHF) aligns Large Language Models (LLMs) with general, aggregate human preferences, it is suboptimal for learning diverse, indivi…

cs.CL2023

CoTEVer: Chain of Thought Prompting Annotation Toolkit for Explanation Verification

Seungone Kim, Se June Joo, Yul Jang +2

Chain-of-thought (CoT) prompting enables large language models (LLMs) to solve complex reasoning tasks by generating an explanation before the final prediction. Despite it's promis…