Human Feedback and Eval Paper Explorer

Match reason: Keyword overlap 2/2 across title and protocol fields.

• In this paper, we conduct a systematic evaluation of several LLMs, including GPT-3.5/4, FLAN-T5, and Llama 3, across zero-shot, few-shot, and explanation-based prompting settings.
• To enable principled evaluation under real-world topic diversity, we introduce a new dataset of out-of-domain news headlines covering diverse subjects.

Match reason: Keyword overlap 2/2 across title and protocol fields.

• Watermarking is a promising approach to distinguish machine-generated text from human text, embedding statistical signals in the output that are ideally undetectable to humans.

Match reason: Keyword overlap 1/2 across title and protocol fields.

• This work introduces a toolchain for applying Reinforcement Learning (RL), specifically the Deep Deterministic Policy Gradient (DDPG) algorithm, in safety-critical real-world environments.
• RL provides a viable solution, however, safety concerns, such as excessive pressure rise rates, must be addressed when applying to HCCI.

Match reason: Keyword overlap 1/2 across title and protocol fields.

• However, existing datasets exhibit a strong long-tail distribution in scenario density, where common low-density cases dominate and safety-critical high-density cases are severely underrepresented.
• This imbalance limits model robustness and hides failure modes when standard evaluations average errors across all scenarios.

Match reason: Keyword overlap 1/2 across title and protocol fields.

Match reason: Keyword overlap 1/2 across title and protocol fields.

• Experimental evaluations across four datasets of relational reasoning demonstrate that PoT surpasses state-of-the-art baselines by a significant margin (up to 21.3%) without requiring fine-tuning or extensive LLM calls.

Match reason: Keyword overlap 1/2 across title and protocol fields.

• EventFlow is simple to implement and achieves a 20%-53% lower forecast error than the nearest baseline on standard TPP benchmarks while simultaneously using fewer model calls at sampling time.

Match reason: Matched by broad semantic/index fallback.

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

Match reason: Matched by broad semantic/index fallback.

• We evaluate explainable ML for screening of Alzheimer's disease and related dementias (ADRD) and severity prediction using benchmark DementiaBank speech (N = 291, 64% female, 69.8 (SD = 8.6) years).

Match reason: Matched by broad semantic/index fallback.

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

Match reason: Matched by broad semantic/index fallback.

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