Perplexity + Automatic Metrics Metric Papers

Updated from current HFEPX corpus (Feb 27, 2026). This page tracks 17 papers for Perplexity + Automatic Metrics Metric Papers. Dominant protocol signals include automatic metrics, simulation environments, with frequent benchmark focus on GSM8K, Retrieval and metric focus on perplexity, accuracy. Use the grounded sections below to prioritize reproducible protocol choices, benchmark-matched comparisons, and judge-vs-human evaluation checks.

Top Papers Reporting This Metric

• NoRA: Breaking the Linear Ceiling of Low-Rank Adaptation via Manifold Expansion

  Hung-Hsuan Chen · Feb 26, 2026

  Automatic Metrics Math

  On the SlimOrca benchmark, NoRA breaks this linear barrier: NoRA remarkably at rank 64 (PPL 3.89) outperforms LoRA at rank 512 (PPL 3.90), demonstrating superior spectral efficiency.

• SPQ: An Ensemble Technique for Large Language Model Compression

  Jiamin Yao, Eren Gultepe · Feb 20, 2026

  Automatic MetricsSimulation Env MathCoding

  Applied to LLaMA-2-7B, SPQ achieves up to 75% memory reduction while maintaining or improving perplexity (e.g., WikiText-2 5.47 to 4.91) and preserving accuracy on downstream benchmarks such as C4, TruthfulQA, and GSM8K.

• Sign Lock-In: Randomly Initialized Weight Signs Persist and Bottleneck Sub-Bit Model Compression

  Akira Sakai, Yuma Ichikawa · Feb 19, 2026

  Automatic Metrics General

  Sub-bit model compression seeks storage below one bit per weight; as magnitudes are aggressively compressed, the sign bit becomes a fixed-cost bottleneck.

• Training Models on Dialects of Translationese Shows How Lexical Diversity and Source-Target Syntactic Similarity Shape Learning

  Jenny Kunz · Feb 18, 2026

  Automatic Metrics Multilingual

  Machine-translated data is widely used in multilingual NLP, particularly when native text is scarce.

• *-PLUIE: Personalisable metric with Llm Used for Improved Evaluation

  Quentin Lemesle, Léane Jourdan, Daisy Munson, Pierre Alain, Jonathan Chevelu · Feb 17, 2026

  Automatic Metrics General

  Evaluating the quality of automatically generated text often relies on LLM-as-a-judge (LLM-judge) methods.

• Fine-Refine: Iterative Fine-grained Refinement for Mitigating Dialogue Hallucination

  Xiangyan Chen, Yujian Gan, Matthew Purver · Feb 17, 2026

  Automatic Metrics General

  The tendency for hallucination in current large language models (LLMs) negatively impacts dialogue systems.

• Scaling Beyond Masked Diffusion Language Models

  Subham Sekhar Sahoo, Jean-Marie Lemercier, Zhihan Yang, Justin Deschenaux, Jingyu Liu · Feb 16, 2026

  Automatic Metrics MathLaw

  Among discrete diffusion approaches, Masked diffusion currently dominates, largely driven by strong perplexity on language modeling benchmarks.

• Fast-weight Product Key Memory

  Tianyu Zhao, Llion Jones · Jan 2, 2026

  Automatic Metrics General

  Notably, in Needle-in-a-Haystack evaluations, FwPKM generalizes to 128K-token contexts despite being trained on only 4K-token sequences.

• Reconstructing KV Caches with Cross-layer Fusion For Enhanced Transformers

  Hongzhan Lin, Zhiqi Bai, Xinmiao Zhang, Sen Yang, Xiang Li · Dec 3, 2025

  Automatic Metrics Coding

  Transformer decoders have achieved strong results across tasks, but the memory required for the KV cache becomes prohibitive at long sequence lengths.

• Randomized Masked Finetuning: An Efficient Way to Mitigate Memorization of PIIs in LLMs

  Kunj Joshi, David A. Smith · Dec 2, 2025

  Automatic Metrics General

  We present MaxTER, a Pareto-optimal evaluation framework for assessing privacy-utility tradeoffs, and show the performance of RMFT vs Deduplication by Area Under The Response Curve (AURC) metric.

• Empathetic Cascading Networks: A Multi-Stage Prompting Technique for Reducing Social Biases in Large Language Models

  Wangjiaxuan Xin · Nov 24, 2025

  Automatic Metrics General

  This report presents the Empathetic Cascading Networks (ECN) framework, a multi-stage prompting method designed to enhance the empathetic and inclusive capabilities of large language models.

• CoSpaDi: Compressing LLMs via Calibration-Guided Sparse Dictionary Learning

  Denis Makhov, Dmitriy Shopkhoev, Magauiya Zhussip, Ammar Ali, Stamatios Lefkimmiatis · Sep 26, 2025

  Automatic Metrics General

  Post-training compression of large language models (LLMs) often relies on low-rank weight approximations that represent each column of the weight matrix in a shared low-dimensional subspace.

• Share Your Attention: Transformer Weight Sharing via Matrix-based Dictionary Learning

  Magauiya Zhussip, Dmitriy Shopkhoev, Ammar Ali, Stamatios Lefkimmiatis · Aug 6, 2025

  Automatic Metrics General

  Experiments across scales (100M-700M parameters) show that MASA achieves better benchmark accuracy and perplexity than GQA, low-rank baselines and recent Repeat-all-over/Sequential sharing at comparable parameter budgets.

• DeVisE: Behavioral Testing of Medical Large Language Models

  Camila Zurdo Tagliabue, Heloisa Oss Boll, Aykut Erdem, Erkut Erdem, Iacer Calixto · Jun 18, 2025

  Automatic Metrics Medicine

  Large language models (LLMs) are increasingly applied in clinical decision support, yet current evaluations rarely reveal whether their outputs reflect genuine medical reasoning or superficial correlations.

• Watermarking Degrades Alignment in Language Models: Analysis and Mitigation

  Apurv Verma, NhatHai Phan, Shubhendu Trivedi · Jun 4, 2025

  Automatic Metrics General

  In practice, sampling as few as two to four candidates largely restores unwatermarked alignment performance in truthfulness, safety, and helpfulness, without hurting watermark detection.

• Esoteric Language Models: Bridging Autoregressive and Masked Diffusion LLMs

  Subham Sekhar Sahoo, Zhihan Yang, Yash Akhauri, Johnna Liu, Deepansha Singh · Jun 2, 2025

  Automatic Metrics MathCoding

  Diffusion-based language models offer a compelling alternative to autoregressive (AR) models by enabling parallel and controllable generation.

• Bayesian Attention Mechanism: A Probabilistic Framework for Positional Encoding and Context Length Extrapolation

  Arthur S. Bianchessi, Yasmin C. Aguirre, Rodrigo C. Barros, Lucas S. Kupssinskü · May 28, 2025

  Automatic Metrics General

  However, existing PE methods lack theoretical clarity and rely on limited evaluation metrics to substantiate their extrapolation claims.

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