Human Feedback and Eval Paper Explorer

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

• Existing artificial intelligence approaches typically optimize individual components of phenotyping but do not operationalize the full clinical workflow of extracting features from clinical text, standardizing them to Human Phenotype…
• Using clinician-curated HPO terms as the gold standard, RARE-PHENIX consistently outperformed a state-of-the-art deep learning baseline (PhenoBERT) across ontology-based similarity and precision-recall-F1 metrics in end-to-end evaluation…

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

• In this work, we propose "Pyramid MoA", a hierarchical Mixture-of-Agents architecture that uses a lightweight Router to dynamically escalate queries only when necessary.
• On the GSM8K benchmark, our system achieves 93.0% accuracy, effectively matching the Oracle baseline (98.0%) while reducing compute costs by 61%.

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

• Personal AI agents incur substantial cost via repeated LLM calls.
• We show existing caching methods fail: GPTCache achieves 37.9% accuracy on real benchmarks; APC achieves 0-12%.

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

• The framework was evaluated on five lexically heterogeneous clinical concepts against a manually curated benchmark and gold-standard concept sets.
• Results Across all concepts, CUICurate produced substantially larger and more complete concept sets than the manual benchmarks whilst matching human precision.

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

• Our evaluation demonstrates that OrthoDiffusion achieves excellent performance in the segmentation of 11 knee structures and the detection of 8 knee abnormalities.

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• On the full 50-song evaluation at a 0.750 threshold, recall was 39.4% and precision 25.8% .

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

• Lower-scoring outputs are more likely to contain errors, enabling automatic prioritization of limited human review bandwidth.
• We also introduce one of the first public LLM Structured Output benchmarks with reliable ground-truth values.

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

• Enterprise Retrieval-Augmented Generation (RAG) assistants operate in multi-turn, case-based workflows such as technical support and IT operations, where evaluation must reflect operational constraints, structured identifiers (e.g., error…
• We present a case-aware LLM-as-a-Judge evaluation framework for enterprise multi-turn RAG systems.

Match reason: Title directly matches "precision".

• An out-of-distribution (OOD) evaluation on a document published after the model's training cutoff confirms these gains are not memorization artifacts, achieving 0.723 bpb on unseen text.

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

• Data collection involved 60,000 raw posts from various Persian social media platforms, followed by rigorous preprocessing and hybrid annotation combining ChatGPT-based few-shot prompting with human verification.
• We benchmarked several models, including BiLSTM, XLM-RoBERTa (with LoRA and AdaLoRA adaptations), FaBERT, SBERT-based architectures, and the Persian-specific TookaBERT (Base and Large).

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

• In benchmarks across 20 Newsgroups, BBC News, AG News, and Arxiv, TriTopic achieves the highest NMI on every dataset (mean NMI 0.575 vs.

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

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

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• This work presents a comparative analysis of BLEU, an n-gram-based metric, and ChrF++, a character-based metric, for MT evaluation in ELRL settings.

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

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

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

• The qualitative evaluation noted better extraction and discrimination and theological precision.

Match reason: Title directly matches "precision".

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

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

• This paper establishes a fundamental convergence: Schema-Guided Dialogue (SGD) and the Model Context Protocol (MCP) represent two manifestations of a unified paradigm for deterministic, auditable LLM-agent interaction.

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

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

Match reason: Title directly matches "precision".

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

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

• When deterministic scoring cannot resolve an ambiguity, the system escalates to multimodal and constrained large-language-model reasoning, followed by a single human-in-the-loop (HITL) review step.
• By prioritizing deterministic rules, clear decision tracking, and retaining unresolved cases for human review, the framework provides a practical foundation for downstream manufacturing automation in real-world industrial environments.