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research #llm 📝 BlogAnalyzed: Jan 10, 2026 05:00

Controlling LLM Output Variation: An Empirical Look at Temperature, Top-p, Top-k, and Repetition Penalty

Published:Jan 9, 2026 16:34

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1 min read

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Zenn LLM

Analysis

This article provides a hands-on exploration of key LLM output parameters, focusing on their impact on text generation variability. By using a minimal experimental setup without relying on external APIs, it offers a practical understanding of these parameters for developers. The limitation of not assessing model quality is a reasonable constraint given the article's defined scope.

Key Takeaways

•The article demonstrates the behavioral differences of Temperature, Top-p, and Top-k sampling strategies.
•It utilizes a minimal experimental setup based on Python and NumPy.
•The focus is on understanding parameter effects, not evaluating overall model performance.

Reference

“本記事のコードは、Temperature / Top-p / Top-k の挙動差を API なしで体感する最小実験です。”

Permalink Zenn LLM

Research Paper #Retrieval-Augmented Generation (RAG)🔬 ResearchAnalyzed: Jan 3, 2026 06:12

AdaGReS: Redundancy-Aware Context Selection for RAG

Published:Dec 31, 2025 18:48

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1 min read

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ArXiv

Analysis

This paper addresses a critical issue in Retrieval-Augmented Generation (RAG): the inefficiency of standard top-k retrieval, which often includes redundant information. AdaGReS offers a novel solution by introducing a redundancy-aware context selection framework. This framework optimizes a set-level objective that balances relevance and redundancy, employing a greedy selection strategy under a token budget. The key innovation is the instance-adaptive calibration of the relevance-redundancy trade-off parameter, eliminating manual tuning. The paper's theoretical analysis provides guarantees for near-optimality, and experimental results demonstrate improved answer quality and robustness. This work is significant because it directly tackles the problem of token budget waste and improves the performance of RAG systems.

Key Takeaways

•Addresses the problem of redundant context in RAG.
•Proposes AdaGReS, a redundancy-aware context selection framework.
•Employs a greedy selection strategy with a token budget.
•Features instance-adaptive calibration to eliminate manual tuning.
•Demonstrates improved answer quality and robustness in experiments.

Reference

“AdaGReS introduces a closed-form, instance-adaptive calibration of the relevance-redundancy trade-off parameter to eliminate manual tuning and adapt to candidate-pool statistics and budget limits.”

Permalink ArXiv

Research Paper #Drug Discovery, Machine Learning, Bayesian Methods 🔬 ResearchAnalyzed: Jan 3, 2026 06:25

DTI-GP: Bayesian Drug-Target Interaction Prediction

Published:Dec 31, 2025 11:55

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1 min read

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ArXiv

Analysis

This paper introduces DTI-GP, a novel approach for predicting drug-target interactions using deep kernel Gaussian processes. The key contribution is the integration of Bayesian inference, enabling probabilistic predictions and novel operations like Bayesian classification with rejection and top-K selection. This is significant because it provides a more nuanced understanding of prediction uncertainty and allows for more informed decision-making in drug discovery.

Key Takeaways

Reference

“DTI-GP outperforms state-of-the-art solutions, and it allows (1) the construction of a Bayesian accuracy-confidence enrichment score, (2) rejection schemes for improved enrichment, and (3) estimation and search for top-$K$ selections and ranking with high expected utility.”

Permalink ArXiv

Paper #llm 🔬 ResearchAnalyzed: Jan 3, 2026 19:00

Flexible Keyword-Aware Top-k Route Search

Published:Dec 29, 2025 09:10

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1 min read

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ArXiv

Analysis

This paper addresses the limitations of LLMs in route planning by introducing a Keyword-Aware Top-k Routes (KATR) query. It offers a more flexible and comprehensive approach to route planning, accommodating various user preferences like POI order, distance budgets, and personalized ratings. The proposed explore-and-bound paradigm aims to efficiently process these queries. This is significant because it provides a practical solution to integrate LLMs with route planning, improving user experience and potentially optimizing travel plans.

Key Takeaways

•Addresses limitations of LLMs in route planning.
•Introduces Keyword-Aware Top-k Routes (KATR) query.
•Offers flexibility in POI order, distance, and ratings.
•Employs an explore-and-bound paradigm for efficiency.
•Aims to improve user experience and optimize travel plans.

Reference

“The paper introduces the Keyword-Aware Top-$k$ Routes (KATR) query that provides a more flexible and comprehensive semantic to route planning that caters to various user's preferences including flexible POI visiting order, flexible travel distance budget, and personalized POI ratings.”

Permalink ArXiv

Research Paper #Adversarial Robustness, Neural Ranking, Information Retrieval 🔬 ResearchAnalyzed: Jan 3, 2026 16:08

RobustMask: Certified Robustness for Neural Ranking

Published:Dec 29, 2025 08:51

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1 min read

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ArXiv

Analysis

This paper addresses the critical vulnerability of neural ranking models to adversarial attacks, a significant concern for applications like Retrieval-Augmented Generation (RAG). The proposed RobustMask defense offers a novel approach combining pre-trained language models with randomized masking to achieve certified robustness. The paper's contribution lies in providing a theoretical proof of certified top-K robustness and demonstrating its effectiveness through experiments, offering a practical solution to enhance the security of real-world retrieval systems.

Key Takeaways

•Proposes RobustMask, a novel defense against adversarial attacks on neural ranking models.
•Combines pre-trained language models with randomized masking for robustness.
•Provides a theoretical proof of certified top-K robustness.
•Demonstrates effectiveness in certifying a significant portion of ranked documents against perturbations.

Reference

“RobustMask successfully certifies over 20% of candidate documents within the top-10 ranking positions against adversarial perturbations affecting up to 30% of their content.”

Permalink ArXiv

Research Paper #Computer Vision, Face Clustering, Transformer 🔬 ResearchAnalyzed: Jan 3, 2026 16:23

Sparse Differential Transformer for Robust Face Clustering

Published:Dec 27, 2025 14:39

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1 min read

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ArXiv

Analysis

This paper addresses the problem of noise in face clustering, a critical issue for real-world applications. The authors identify limitations in existing methods, particularly the use of Jaccard similarity and the challenges of determining the optimal number of neighbors (Top-K). The core contribution is the Sparse Differential Transformer (SDT), designed to mitigate noise and improve the accuracy of similarity measurements. The paper's significance lies in its potential to improve the robustness and performance of face clustering systems, especially in noisy environments.

Key Takeaways

•Addresses the problem of noise in face clustering.
•Proposes a Sparse Differential Transformer (SDT) to improve similarity measurements.
•Achieves state-of-the-art (SOTA) performance on multiple datasets.
•Focuses on improving the robustness of face clustering in noisy environments.

Reference

“The Sparse Differential Transformer (SDT) is proposed to eliminate noise and enhance the model's anti-noise capabilities.”

Permalink ArXiv

Research #Attention 🔬 ResearchAnalyzed: Jan 10, 2026 13:22

Initial Study Explores Sparse Attention's Potential and Hurdles

Published:Dec 3, 2025 06:44

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1 min read

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ArXiv

Analysis

The article's focus on sparse attention indicates an investigation into efficient transformer architectures. A preliminary study suggests the field is still exploring the tradeoffs between performance and computational efficiency.

Key Takeaways

•Investigates Native Top-$k$ Sparse Attention.
•Focuses on potential performance benefits in Transformers.
•Highlights ongoing challenges related to implementation.

Reference

“The study is preliminary and available on ArXiv.”

Permalink ArXiv

Research #llm 📝 BlogAnalyzed: Dec 29, 2025 09:40

How to generate text: Decoding Methods for Language Generation with Transformers

Published:Mar 1, 2020 00:00

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1 min read

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Hugging Face

Analysis

This article from Hugging Face likely discusses different decoding methods used in Transformer-based language models for text generation. It would probably cover techniques like greedy search, beam search, and sampling methods (e.g., top-k, top-p). The analysis would likely explain the trade-offs between these methods, such as the balance between text quality (fluency, coherence) and diversity. It might also touch upon the computational cost associated with each method and provide practical guidance on choosing the appropriate decoding strategy for different use cases. The article's focus is on the practical application of these methods within the Hugging Face ecosystem.

Key Takeaways

•Different decoding methods impact text quality and diversity.
•Greedy search is fast but can lead to repetitive text.
•Beam search improves quality but is more computationally expensive.
•Sampling methods offer more diverse outputs.

Reference

“The article likely includes examples of how different decoding methods affect the generated text.”

Permalink Hugging Face

Research #Privacy 📝 BlogAnalyzed: Dec 29, 2025 08:06

Practical Differential Privacy at LinkedIn with Ryan Rogers - #346

Published:Feb 7, 2020 19:39

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1 min read

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Practical AI

Analysis

This article discusses a podcast episode featuring Ryan Rogers, a Senior Software Engineer at LinkedIn. The core topic revolves around the implementation of differential privacy at LinkedIn to protect user data while enabling data scientists to perform exploratory analytics. The conversation focuses on Rogers' paper, "Practical Differentially Private Top-k Selection with Pay-what-you-get Composition." The discussion highlights the use of the exponential mechanism, a common algorithm in differential privacy, and its relationship to Gumbel noise. The article suggests a practical application of differential privacy in a real-world scenario, emphasizing the balance between data utility and user privacy.

Key Takeaways

•LinkedIn uses differential privacy to protect user data while allowing data analysis.
•The discussion centers around the "Practical Differentially Private Top-k Selection with Pay-what-you-get Composition" paper.
•The exponential mechanism and Gumbel noise are key components of the discussed differential privacy implementation.

Reference

“The article doesn't contain a direct quote, but it discusses the content of a podcast episode.”

Permalink Practical AI

Controlling LLM Output Variation: An Empirical Look at Temperature, Top-p, Top-k, and Repetition Penalty

Analysis

Key Takeaways

AdaGReS: Redundancy-Aware Context Selection for RAG

Analysis

Key Takeaways

DTI-GP: Bayesian Drug-Target Interaction Prediction

Analysis

Key Takeaways

Flexible Keyword-Aware Top-k Route Search

Analysis

Key Takeaways

RobustMask: Certified Robustness for Neural Ranking

Analysis

Key Takeaways

Sparse Differential Transformer for Robust Face Clustering

Analysis

Key Takeaways

Initial Study Explores Sparse Attention's Potential and Hurdles

Analysis

Key Takeaways

How to generate text: Decoding Methods for Language Generation with Transformers

Analysis

Key Takeaways

Practical Differential Privacy at LinkedIn with Ryan Rogers - #346

Analysis

Key Takeaways

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