#knowledge-intensive-tasks News & Analysis

4 articles tagged with #knowledge-intensive-tasks. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

4 articles

AINeutralarXiv – CS AI · Jun 97/10

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AI Scientists Are Only as Good as Their Evidence: A Stratified Ablation of Proprietary Data and Reasoning Skills in Drug-Asset Valuation

Researchers demonstrate that AI agents' performance in drug-asset valuation is fundamentally limited by access to proprietary data rather than reasoning quality alone. A three-arm experiment shows that adding reasoning scaffolds and structured tools improves calibration but cannot overcome gaps in underlying evidence, with proprietary datasets enabling 96% recovery of expert valuations versus 38% for public-data-only systems.

AIBullisharXiv – CS AI · Apr 147/10

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Disco-RAG: Discourse-Aware Retrieval-Augmented Generation

Researchers introduce Disco-RAG, a discourse-aware framework that enhances Retrieval-Augmented Generation (RAG) systems by explicitly modeling discourse structures and rhetorical relationships between retrieved passages. The method achieves state-of-the-art results on question answering and summarization tasks without fine-tuning, demonstrating that structural understanding of text significantly improves LLM performance on knowledge-intensive tasks.

AINeutralarXiv – CS AI · Jun 236/10

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CalVerT: Augmenting Agents with Calibrated Verifier Telemetry Improves Action and Learning in Knowledge-Intensive Tasks

CalVerT is a new framework that enhances LLM agents by providing calibrated confidence scores and grounding verification, helping agents distinguish between reliable and uncertain knowledge during question-answering tasks. The approach reduces both inaccurate confident answers and wasteful over-retrieval, improving performance across multiple QA benchmarks without requiring additional training.

AINeutralApple Machine Learning · Apr 136/10

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Cram Less to Fit More: Training Data Pruning Improves Memorization of Facts

Researchers present a data pruning technique that improves how large language models memorize factual knowledge by optimizing training data distribution. The work, grounded in information-theoretic analysis, addresses the gap between theoretical model capacity and actual factual accuracy, offering practical methods to reduce hallucinations in knowledge-intensive tasks.