A new sequential interaction framework lets LLMs propose questions to forums, with simulations on real Stack Exchange data showing players can reach roughly half the utility of an ideal full-information scenario despite incentive misalignment.
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Chris Kamphuis, Arjen P
6 Pith papers cite this work, alongside 4,442 external citations. Polarity classification is still indexing.
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representative citing papers
SWE-RL uses RL on software evolution data to train LLMs achieving 41% on SWE-bench Verified with generalization to other reasoning tasks.
LightSTAR achieves state-of-the-art accuracy in visual document retrieval by decomposing the task into LLM-free high-recall candidate selection and vision-adaptive semantic refinement on candidates, cutting end-to-end latency several-fold.
Large-scale evaluation shows retrieval-augmented generation yields only marginal and inconsistent gains (1-2 points) over no-retrieval baselines in biomedical QA, with model choice dominating retriever or corpus effects.
A q-log odds variant of BM25 raises NDCG@10 by 89% relative on CodeSearchNet Go under fixed generic tokenization while recovering standard BM25 at q=1.
RAPTOR introduces a tree-organized retrieval method using recursive abstractive summaries, achieving a 20% absolute accuracy improvement on the QuALITY benchmark when paired with GPT-4.
citing papers explorer
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From Competition to Collaboration: Designing Sustainable Mechanisms Between LLMs and Online Forums
A new sequential interaction framework lets LLMs propose questions to forums, with simulations on real Stack Exchange data showing players can reach roughly half the utility of an ideal full-information scenario despite incentive misalignment.
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SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution
SWE-RL uses RL on software evolution data to train LLMs achieving 41% on SWE-bench Verified with generalization to other reasoning tasks.
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LightSTAR: Efficient Visual Document Retrieval via Lightweight Selection with Vision-Adaptive Refinement
LightSTAR achieves state-of-the-art accuracy in visual document retrieval by decomposing the task into LLM-free high-recall candidate selection and vision-adaptive semantic refinement on candidates, cutting end-to-end latency several-fold.
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When Retrieval Doesn't Help: A Large-Scale Study of Biomedical RAG
Large-scale evaluation shows retrieval-augmented generation yields only marginal and inconsistent gains (1-2 points) over no-retrieval baselines in biomedical QA, with model choice dominating retriever or corpus effects.
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Improving BM25 Code Retrieval Under Fixed Generic Tokenization: Adaptive q-Log Odds as a Drop-In BM25 Fix
A q-log odds variant of BM25 raises NDCG@10 by 89% relative on CodeSearchNet Go under fixed generic tokenization while recovering standard BM25 at q=1.
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RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval
RAPTOR introduces a tree-organized retrieval method using recursive abstractive summaries, achieving a 20% absolute accuracy improvement on the QuALITY benchmark when paired with GPT-4.