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Why Chirpz
Why Using Chirpz
Traditional search drowns you in noise or misses critical papers. Chirpz understands your research to surface exactly what you need.
What are the latest advances in diffusion models?
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research_draft.pdf
12 pages • Uploaded
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Missing citations to support sections 3.1 and 3.3.
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280M+
Papers Across Databases
One Search, All Sources
Search 280M+ papers across PubMed, arXiv, academic journals, and conference proceedings — all in one query.
1
Attention Is All You Need
98
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BERT: Pre-training of Deep...
95
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Language Models are Few...
92
Cut through the Noise
AI reads, ranks, and filters papers by true relevance to your work. Helps you read the most important papers first.
AI Snapshot
Key Contribution: Introduces self-attention mechanism replacing RNNs
Relevance: Foundational work for your transformer research
Skip the Skimming
Get instant AI summaries of every paper. Decide what to read in seconds, not hours.
Attention Is All You Need
Vaswani, A., Shazeer, N., et al.
2017 • NeurIPS
Cite With Confidence
Every result is real and verified — zero hallucination. Get complete metadata, ready to export BibTeX, and more.
How it Works
Ask, Discover, Review, Cite.
Zero friction. Total focus.
STEP 01
Ask or Upload
Type your research question or upload a file for analysis. The AI understands your context — no complex search syntax needed.
STEP 02
AI Scopes Research
The AI Instantly extracts key research scopes and crafts smart search strategies — so your research starts targeted and fast.
Generating
STEP 03
Discover Papers
Relevant papers are discovered in real-time and ranked by relevance to your research scope.
STEP 04
Review & Cite
Browse, review, filter, and dive deep into discovered papers — all with rich, accurate metadata. Export citations with a single click.
Learning Conformal Abstention Policies for Adaptive Risk Management in Large Language and Vision-Language Models
Sina Tayebati, Divake Kumar, Nastaran Darabi, Dinithi Jayasuriya, Ranganath Krishnan, Amit Ranjan Trivedi
2025-02-08
arXiv (Cornell University)
Cited by 8
Type preprint
Open Access
Large Language and Vision-Language Models (LLMs/VLMs) are increasingly used in safety-critical applications, yet their opaque decision-making complicates risk assessment and reliability. Uncertainty quantification (UQ) helps assess prediction confidence and enables abstention when uncertainty is high. Conformal prediction (CP), a leading UQ method, provides statistical guarantees but relies on static thresholds, which fail to adapt to task complexity and evolving data distributions, leading to suboptimal trade-offs in accuracy, coverage, and informativeness. To address this, we propose learnable conformal abstention, integrating reinforcement learning (RL) with CP to optimize abstention thresholds dynamically. By treating CP thresholds as adaptive actions, our approach balances multiple objectives, minimizing prediction set size while maintaining reliable coverage. Extensive evaluations across diverse LLM/VLM benchmarks show our method outperforms Least Ambiguous Classifiers (LAC) and Adaptive Prediction Sets (APS), improving accuracy by up to 3.2%, boosting AUROC for hallucination detection by 22.19%, enhancing uncertainty-guided selective generation (AUARC) by 21.17%, and reducing calibration error by 70%-85%. These improvements hold across multiple models and datasets while consistently meeting the 90% coverage target, establishing our approach as a more effective and flexible solution for reliable decision-making in safety-critical applications. The code is available at: https://github.com/sinatayebati/vlm-uncertainty.
Who Benefits
Who is Chirpz for
From individual researchers to entire institutions, Chirpz empowers everyone in the research ecosystem.
Explore emerging tech, track competitive research, and validate approaches with comprehensive analysis.
Streamline drug discovery, monitor clinical trials, and ensure comprehensive patent searches.
Focus on insights, discover papers faster and manage citations effortlessly.
Build strong foundations for your thesis with accurate citations and AI-guided discovery.
Make progress on your research, today
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FAQ
Frequently Asked Questions
Everything you need to know about Chirpz
Yes! Upload your PDF draft and Chirpz identifies gaps in your literature review, detects sections lacking citations, and suggests relevant papers to strengthen your claims.
Chirpz searches 280M+ papers across PubMed, arXiv, journals, and conferences all at once. Ask your research question naturally — no need for complex search syntax or multiple database searches.
Chirpz reads, ranks, and filters papers by true relevance to your research, not just citation counts. It understands details of each paper and helps you cut through the noise.
Complete metadata (title, abstract, authors, date, journal, citations), AI-generated summaries, ready-to-export BibTeX citations, and PDF previews when available.
Absolutely. Every result is real and verified — zero hallucinations. All papers come from established databases with authentic, accurate metadata you can cite with confidence.
Yes! Start with 200 free credits per month. Pro plan unlocks unlimited requests and advanced features for power users and research teams.