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Found 7 papers, 5 papers with code
The BiGGen Bench: A Principled Benchmark for Fine-grained Evaluation of Language Models with Language Models
To overcome these limitations, we introduce the BiGGen Bench, a principled generation benchmark designed to thoroughly evaluate nine distinct capabilities of LMs across 77 diverse tasks.
Aligning to Thousands of Preferences via System Message Generalization
Using this dataset, we train a 7B LLM called Janus and test it on 921 prompts from 5 benchmarks (AlpacaEval 2. 0, FLASK, Koala, MT-Bench, and Self-Instruct) by adding various unseen system messages that reflect user preferences.
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
Text-to-SQL models are pivotal for making Electronic Health Records (EHRs) accessible to healthcare professionals without SQL knowledge.
Prometheus-Vision: Vision-Language Model as a Judge for Fine-Grained Evaluation
Assessing long-form responses generated by Vision-Language Models (VLMs) is challenging.
Volcano: Mitigating Multimodal Hallucination through Self-Feedback Guided Revision
Building on this approach, we introduce Volcano, a multimodal self-feedback guided revision model.
Ranked #76 on
Visual Question Answering
on MM-Vet
Zero-Shot Dense Video Captioning by Jointly Optimizing Text and Moment
This is accomplished by introducing a soft moment mask that represents a temporal segment in the video and jointly optimizing it with the prefix parameters of a language model.
LIQUID: A Framework for List Question Answering Dataset Generation
Question answering (QA) models often rely on large-scale training datasets, which necessitates the development of a data generation framework to reduce the cost of manual annotations.
Ranked #1 on
Question Answering
on MultiSpanQA
