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20242026
most citedPrometheus 2: An Open Source Language Model Specialized in Evaluating Other Language Models

7 citations · 10 across the 10 of their papers we have counts for

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cs.AI2024

Semantic Skill Grounding for Embodied Instruction-Following in Cross-Domain Environments

Sangwoo Shin, Seunghyun Kim, Youngsoo Jang +2

In embodied instruction-following (EIF), the integration of pretrained language models (LMs) as task planners emerges as a significant branch, where tasks are planned at the skill…

cs.CL2024

Learning to Explore and Select for Coverage-Conditioned Retrieval-Augmented Generation

Takyoung Kim, Kyungjae Lee, Young Rok Jang +4

Interactions with large language models (LLMs) often yield long and detailed responses, leveraging both parametric knowledge and retrieval-augmented generation (RAG). While these r…

cs.CL2024

The BiGGen Bench: A Principled Benchmark for Fine-grained Evaluation of Language Models with Language Models

Seungone Kim, Juyoung Suk, Ji Yong Cho +29

As language models (LMs) become capable of handling a wide range of tasks, their evaluation is becoming as challenging as their development. Most generation benchmarks currently as…

cs.CL2024★ 2 cited

LG AI Research & KAIST at EHRSQL 2024: Self-Training Large Language Models with Pseudo-Labeled Unanswerable Questions for a Reliable Text-to-SQL System on EHRs

Yongrae Jo, Seongyun Lee, Minju Seo +2

Text-to-SQL models are pivotal for making Electronic Health Records (EHRs) accessible to healthcare professionals without SQL knowledge. With the advancements in large language mod…

cs.CL2024★ 7 cited

Prometheus 2: An Open Source Language Model Specialized in Evaluating Other Language Models

Seungone Kim, Juyoung Suk, Shayne Longpre +7

Proprietary LMs such as GPT-4 are often employed to assess the quality of responses from various LMs. However, concerns including transparency, controllability, and affordability s…

cs.CL2024★ 1 cited

Reinforcement Learning from Reflective Feedback (RLRF): Aligning and Improving LLMs via Fine-Grained Self-Reflection

Kyungjae Lee, Dasol Hwang, Sunghyun Park +2

Despite the promise of RLHF in aligning LLMs with human preferences, it often leads to superficial alignment, prioritizing stylistic changes over improving downstream performance o…

cs.CV2024

YTCommentQA: Video Question Answerability in Instructional Videos

Saelyne Yang, Sunghyun Park, Yunseok Jang +1

Instructional videos provide detailed how-to guides for various tasks, with viewers often posing questions regarding the content. Addressing these questions is vital for comprehend…