Hugging Face Trending Papers September 3, 2026

When Retrieval Helps: Selective Retrieval for Single-Turn Mental-Health QA

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The paper investigates how retrieval-augmented generation (RAG) affects single‑turn mental‑health question answering. It finds that always using retrieval improves response specificity but can lower overall quality and increase safety‑related failures. A lightweight selective retrieval policy, guided by psychoeducational, coping, and safety needs, better balances these trade‑offs by activating retrieval only when necessary.

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arXiv Computation and Language

Sep 4

When Retrieval Helps: Selective Retrieval for Single-Turn Mental-Health QA

The paper investigates how retrieval‑augmented generation (RAG) affects single‑turn mental‑health question answering. It introduces a lightweight selective retrieval policy that activates retrieval only when user queries exhibit psychoeducational, coping, or specificity needs, guided by a rule‑based safety trigger. Experiments show that unconditional retrieval improves specificity but degrades overall quality and safety, whereas selective retrieval maintains closed‑book performance for low‑need cases while avoiding these negative effects.

By Hyunseo Oh, Chong-Kwon Kim, Yoonhyuk Choi

llms rag nlp fine-tuning safety

arXiv AI

Jul 9

Healthier LLMs: Retrieval-Augmented Generation for Public Health Question Answering

arXiv:2607. 06641v1 Announce Type: cross Abstract: Large language models (LLMs) achieve promising results on medical question answering benchmarks, yet their use in public health is constrained by hallucinations and the rapid evolution of official guidance.

By Felix Feldman, Joshua Harris, Timothy Laurence, Leo Loman, Ollie Higgins, Fan Grayson, Poonam Soma, Bethany Pace-Bonello, Michael Borowitz, Toby Nonnenmacher

llms rag nlp benchmarks safety

arXiv AI

Jun 30

A Systems-Level Analysis of Sensitivity, Robustness, and Stability in Retrieval-Augmented Generation

arXiv:2606. 28337v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems are often evaluated using final answer accuracy, even though their failures can originate from preprocessing, retrieval, context packing, or generation.

By Bharath Simha Reddy Muthyam

rag

arXiv AI

Jul 29

Retrieval-Augmented Generation in LLMs for Mental Health: Quantifying the Incremental Contribution of Retrieval Within a Layered Safety Architecture

arXiv:2607. 24817v1 Announce Type: cross Abstract: Digital mental health interventions (DMHIs) offer scalable support, but ensuring they accurately detect users' intent during volatile situations can be challenging.

By Anand Gupta, Akshat Surolia, Shubham Mishra, Shakil Imtiaz, Chaitali Sinha

llms rag safety

Hugging Face Trending Papers

Jul 28

Beyond Self-Knowledge: Propagating Uncertainty Across Reasoning and Retrieval in LLMs

Retrieval-augmented generation improves knowledge-intensive question answering, but indiscriminate retrieval can introduce irrelevant evidence and unnecessary computation. We investigate whether verbalized confidence from black-box language models can serve as an actionable signal for retrieval routing.

llms rag nlp benchmarks

arXiv AI

Jul 29

Beyond Self-Knowledge: Propagating Uncertainty Across Reasoning and Retrieval in LLMs

arXiv:2607. 25600v1 Announce Type: cross Abstract: Retrieval-augmented generation improves knowledge-intensive question answering, but indiscriminate retrieval can introduce irrelevant evidence and unnecessary computation.

By Chandan Kumar Sah, Xiaoli Lian, Li Zhang

llms rag nlp benchmarks

One email a morning, machine-written

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When Retrieval Helps: Selective Retrieval for Single-Turn Mental-Health QA

Healthier LLMs: Retrieval-Augmented Generation for Public Health Question Answering

A Systems-Level Analysis of Sensitivity, Robustness, and Stability in Retrieval-Augmented Generation

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Beyond Self-Knowledge: Propagating Uncertainty Across Reasoning and Retrieval in LLMs

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