Introduces a feature-level annotated patent dataset and LLM retrieval-reasoning workflows that outperform embedding baselines on passage retrieval and novel feature identification while avoiding spurious correlations in novelty prediction.
Gonzalez, Hao Zhang, and Ion Stoica
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
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UNVERDICTED 6representative citing papers
Rabtriever distills a generative reranker into an efficient bi-encoder using on-policy JEPA to achieve near-reranker accuracy with linear complexity on rationale-based retrieval.
UNO distills user logs into semi-structured rules and preferences, applies query-and-feedback clustering to handle heterogeneity, quantifies cognitive gaps to filter noise, and builds primary and reflective modules that outperform RAG and memory baselines.
PatMD improves harmful meme detection by retrieving misjudgment risk patterns to guide MLLMs, reporting 8.30% average F1 and 7.71% accuracy gains on 6,626 memes across 5 tasks.
DeInfer adds multiple optimizations to improve parallel inference speed for decomposed LLMs while remaining compatible with existing techniques.
Peerispect extracts claims from peer reviews, retrieves evidence from the manuscript, and verifies them via NLI in a modular pipeline with a visual interface.
citing papers explorer
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Is It Novel and Why? Fine-Grained Patent Novelty Prediction Based on Passage Retrieval
Introduces a feature-level annotated patent dataset and LLM retrieval-reasoning workflows that outperform embedding baselines on passage retrieval and novel feature identification while avoiding spurious correlations in novelty prediction.
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Efficient Rationale-based Retrieval: On-policy Distillation from Generative Rerankers based on JEPA
Rabtriever distills a generative reranker into an efficient bi-encoder using on-policy JEPA to achieve near-reranker accuracy with linear complexity on rationale-based retrieval.
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Improve Large Language Model Systems with User Logs
UNO distills user logs into semi-structured rules and preferences, applies query-and-feedback clustering to handle heterogeneity, quantifies cognitive gaps to filter noise, and builds primary and reflective modules that outperform RAG and memory baselines.
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Fall into a Pit, Gain in a Wit: Cognitive-Guided Harmful Meme Detection via Misjudgment Risk Pattern Retrieval
PatMD improves harmful meme detection by retrieving misjudgment risk patterns to guide MLLMs, reporting 8.30% average F1 and 7.71% accuracy gains on 6,626 memes across 5 tasks.
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DeInfer: Efficient Parallel Inferencing for Decomposed Large Language Models
DeInfer adds multiple optimizations to improve parallel inference speed for decomposed LLMs while remaining compatible with existing techniques.
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Peerispect: Claim Verification in Scientific Peer Reviews
Peerispect extracts claims from peer reviews, retrieves evidence from the manuscript, and verifies them via NLI in a modular pipeline with a visual interface.