Human Feedback and Eval Paper Explorer

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• Improving performance typically requires task-specific fine-tuning, which depends on expensive human labeling and is prone to overfitting.
• Extensive evaluations on Llama, GPT-3.5, and GPT-4 show that Table-LLM-Specialist achieves (1) strong performance across diverse tasks compared to base models, for example, models fine-tuned on GPT-3.5 often surpass GPT-4 level quality; (2)…

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• Abstract shows limited direct human-feedback or evaluation-protocol detail; use as adjacent methodological context.

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• Building on this insight, we introduce the Hybrid-grained Weight Importance Assessment (HyWIA), a novel method that merges fine-grained and coarse-grained evaluations of weight importance for the pruning of LLMs.
• Extensive experiments on LLaMA-V1/V2, Vicuna, Baichuan, and Bloom across various benchmarks demonstrate the effectiveness of HyWIA in pruning LLMs.

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• We then show that a neural network language model's verb passivizability judgments are largely similar to those displayed by humans, suggesting that evidence for these exceptions is available in the linguistic input.

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• Abstract shows limited direct human-feedback or evaluation-protocol detail; use as adjacent methodological context.

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• To enhance video generation quality, we optimize EasyAnimate using reward backpropagation to better align with human preferences.
• The EasyAnimate achieves state-of-the-art performance on both the VBench leaderboard and human evaluation.

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• Abstract shows limited direct human-feedback or evaluation-protocol detail; use as adjacent methodological context.

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• Cross-model evaluation confirms that learned CoTs generalize across architectures, suggesting they encode transferable reasoning steps rather than model-specific artifacts.

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• Experiments demonstrate that our datasets are harder than previous benchmarks.
• In-depth automatic and human evaluations of model-generated explanations provide insights into the promise and deficiency of LLMs for explainable medical QA.

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• Supporting the health and well-being of dynamic populations around the world requires governmental agencies, organizations and researchers to understand and reason over complex relationships between human behavior and local contexts in…
• We evaluate the effectiveness of our approach by benchmarking it on 27 downstream tasks spanning three distinct domains: health indicators, socioeconomic factors, and environmental measurements.

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• Human mobility prediction plays a critical role in applications such as disaster response, urban planning, and epidemic forecasting.
• We validate our approach using large-scale human mobility data from four metropolitan areas in Japan, focusing on predicting individual trajectories over the next 15 days.

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• Safety concerns in large language models (LLMs) have gained significant attention due to their exposure to potentially harmful data during pre-training.
• To address this vulnerability, we propose expanding safety training to cover a broader semantic space of toxic content.

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• Abstract shows limited direct human-feedback or evaluation-protocol detail; use as adjacent methodological context.

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• Notably, RobustRAG achieves certifiable robustness: for certain queries in our evaluation datasets, we can formally certify non-trivial lower bounds on response quality -- even against an adaptive attacker with full knowledge of the defense…

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• Abstract shows limited direct human-feedback or evaluation-protocol detail; use as adjacent methodological context.

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• Abstract shows limited direct human-feedback or evaluation-protocol detail; use as adjacent methodological context.

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• Abstract shows limited direct human-feedback or evaluation-protocol detail; use as adjacent methodological context.