# Enhancing Trust in LLMs: Algorithms for Comparing and Interpreting LLMs arXiv: https://arxiv.org/abs/2406.01943 ยท HTML: https://mlsanity.com/paper/2406.01943/ ## Key facts - Cited by 9 papers in the map (data through 14 Oct 2026). - Builds on 29 of its 66 references that are in the map. - First posted on arXiv 4 Jun 2024. ## Builds on (most cited first, 29 of 29) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020) - [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019) - [CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing](https://mlsanity.com/paper/2305.11738/) (2023) - [TADAM: Task dependent adaptive metric for improved few-shot learning](https://mlsanity.com/paper/1805.10123/) (2018) - [Fairness in Machine Learning: A Survey](https://mlsanity.com/paper/2010.04053/) (2020) - [Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback](https://mlsanity.com/paper/2302.12813/) (2023) - [AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias](https://mlsanity.com/paper/1810.01943/) (2018) - [On the Robustness of Interpretability Methods](https://mlsanity.com/paper/1806.08049/) (2018) - [Generating Training Data with Language Models: Towards Zero-Shot Language Understanding](https://mlsanity.com/paper/2202.04538/) (2022) - [LEEP: A New Measure to Evaluate Transferability of Learned Representations](https://mlsanity.com/paper/2002.12462/) (2020) - [On the (Statistical) Detection of Adversarial Examples](https://mlsanity.com/paper/1702.06280/) (2017) - [Graph Meta Learning via Local Subgraphs](https://mlsanity.com/paper/2006.07889/) (2020) - [Zero-shot Text Classification With Generative Language Models](https://mlsanity.com/paper/1912.10165/) (2019) - [Word Embeddings: A Survey](https://mlsanity.com/paper/1901.09069/) (2019) - [A Survey of Adversarial Defences and Robustness in NLP](https://mlsanity.com/paper/2203.06414/) (2022) - [Few-shot Action Recognition with Implicit Temporal Alignment and Pair Similarity Optimization](https://mlsanity.com/paper/2010.06215/) (2020) - [MVPSNet: Fast Generalizable Multi-view Photometric Stereo](https://mlsanity.com/paper/2305.11167/) (2023) - [A Confidence-based Partial Label Learning Model for Crowd-Annotated Named Entity Recognition](https://mlsanity.com/paper/2305.12485/) (2023) - [Modular Neural Ordinary Differential Equations](https://mlsanity.com/paper/2109.07359/) (2021) - [CUBES: A Parallel Synthesizer for SQL Using Examples](https://mlsanity.com/paper/2203.04995/) (2022) - [JKU-ITS Automobile for Research on Autonomous Vehicles](https://mlsanity.com/paper/2301.06422/) (2023) - [Learning unidirectional coupling using echo-state network](https://mlsanity.com/paper/2303.13562/) (2023) - [A Novel Compensation Algorithm for Thickness Measurement Immune to Lift-Off Variations Using Eddy Current Method](https://mlsanity.com/paper/1905.13544/) (2019) - [A Probabilistic Spectral Analysis of Multivariate Real-Valued Nonstationary Signals](https://mlsanity.com/paper/2007.13855/) (2020) - [A cooperative partial snapshot algorithm for checkpoint-rollback recovery of large-scale and dynamic distributed systems and experimental evaluations](https://mlsanity.com/paper/2103.15285/) (2021) - [A Logic for Veracity](https://mlsanity.com/paper/2302.06164/) (2023) - [Generating Humanoid Multi-Contact through Feasibility Visualization](https://mlsanity.com/paper/2303.08232/) (2023) - [Flexible Bayesian Quantile Analysis of Residential Rental Rates](https://mlsanity.com/paper/2305.13687/) (2023) ## Built on by (most cited first, 9 of 9) - [Evaluation and Benchmarking of LLM Agents: A Survey](https://mlsanity.com/paper/2507.21504/) (2025) - [SQLens: An End-to-End Framework for Error Detection and Correction in Text-to-SQL](https://mlsanity.com/paper/2506.04494/) (2025) - [BioAgents: Democratizing Bioinformatics Analysis with Multi-Agent Systems](https://mlsanity.com/paper/2501.06314/) (2025) - [SuperCoder2.0: Technical Report on Exploring the feasibility of LLMs as Autonomous Programmer](https://mlsanity.com/paper/2409.11190/) (2024) - [Collaborative Participatory Research with LLM Agents in South Asia: An Empirically-Grounded Methodological Initiative and Agenda from Field Evidence in Sri Lanka](https://mlsanity.com/paper/2411.08294/) (2024) - [LLM-Generated Negative News Headlines Dataset: Creation and Benchmarking Against Real Journalism](https://mlsanity.com/paper/2511.11591/) (2025) - [Optimizing Multimodal Language Models through Attention-based Interpretability](https://mlsanity.com/paper/2511.23375/) (2025) - [Asm2SrcEval: Evaluating Large Language Models for Assembly-to-Source Code Translation](https://mlsanity.com/paper/2512.00134/) (2025) - [Automated Marine Biofouling Assessment: Benchmarking Computer Vision and Multimodal LLMs on the Level of Fouling Scale](https://mlsanity.com/paper/2601.20196/) (2026) ## Abstract (arXiv) This paper surveys evaluation techniques to enhance the trustworthiness and understanding of Large Language Models (LLMs). As reliance on LLMs grows, ensuring their reliability, fairness, and transparency is crucial. We explore algorithmic methods and metrics to assess LLM performance, identify weaknesses, and guide development towards more trustworthy applications. Key evaluation metrics include Perplexity Measurement, NLP metrics (BLEU, ROUGE, METEOR, BERTScore, GLEU, Word Error Rate, Character Error Rate), Zero-Shot and Few-Shot Learning Performance, Transfer Learning Evaluation, Adversarial Testing, and Fairness and Bias Evaluation. We introduce innovative approaches like LLMMaps for stratified evaluation, Benchmarking and Leaderboards for competitive assessment, Stratified Analysis for in-depth understanding, Visualization of Blooms Taxonomy for cognitive level accuracy distribution, Hallucination Score for quantifying inaccuracies, Knowledge Stratification Strategy for hierarchical analysis, and Machine Learning Models for Hierarchy Generation. Human Evaluation is highlighted for capturing nuances that automated metrics may miss. These techniques form a framework for evaluating LLMs, aiming to enhance transparency, guide development, and establish user trust. Future papers will describe metric visualization and demonstrate each approach on practical examples.