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Found 5 papers, 5 papers with code
Investigating How Large Language Models Leverage Internal Knowledge to Perform Complex Reasoning
Despite significant advancements, there is a limited understanding of how large language models (LLMs) utilize knowledge for reasoning.
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.
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.
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