MCompassRAG adds topic metadata to chunk representations and uses LLM distillation to train a lightweight topic-aware retriever, reporting 8.24% average information efficiency gain and over 5x lower latency than strong baselines across six benchmarks.
and Ruiz, Francisco J
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SBTA reformulates topic modeling to assign topics at the segment level rather than document level, yielding cleaner topics on a new SemEval-STM dataset created via LLM decomposition and human refinement.
Empirical analysis of 4707 MoltBook posts shows AI-only technical discourse focuses on security, trust, and abstract topics while lacking concrete runtime and project details found in human GitHub discussions.
Topic modeling of 6520 AI papers reveals divergent and sometimes contradictory conceptions of bias within the research community.
citing papers explorer
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MCompassRAG: Topic Metadata as a Semantic Compass for Paragraph-Level Retrieval
MCompassRAG adds topic metadata to chunk representations and uses LLM distillation to train a lightweight topic-aware retriever, reporting 8.24% average information efficiency gain and over 5x lower latency than strong baselines across six benchmarks.
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From Documents to Segments: A Contextual Reformulation for Topic Assignment
SBTA reformulates topic modeling to assign topics at the segment level rather than document level, yielding cleaner topics on a new SemEval-STM dataset created via LLM decomposition and human refinement.
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What Software Engineering Looks Like to AI Agents? -- An Empirical Study of AI-Only Technical Discourse on MoltBook
Empirical analysis of 4707 MoltBook posts shows AI-only technical discourse focuses on security, trust, and abstract topics while lacking concrete runtime and project details found in human GitHub discussions.
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Navigating the muddy waters of bias in artificial intelligence research: Understanding divergent meanings and conceptions
Topic modeling of 6520 AI papers reveals divergent and sometimes contradictory conceptions of bias within the research community.