Enhancing Trust in LLMs: Algorithms for Comparing and Interpreting LLMs

• URL: http://arxiv.org/abs/2406.01943v1
• Date: Tue, 4 Jun 2024 03:54:53 GMT
• Title: Enhancing Trust in LLMs: Algorithms for Comparing and Interpreting LLMs
• Authors: Nik Bear Brown,
• Abstract summary: This paper surveys evaluation techniques to enhance the trustworthiness and understanding of Large Language Models (LLMs) 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.
• Score: 1.0878040851638
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Decoding AI Judgment: How LLMs Assess News Credibility and Bias [0.0]
  Large Language Models (LLMs) are increasingly used to assess news credibility, yet little is known about how they make these judgments. This study benchmarks the reliability and political classifications of state-of-the-art LLMs against structured, expert-driven rating systems. We uncover patterns in how LLMs associate credibility with specific linguistic features by examining keyword frequency, contextual determinants, and rank distributions.
  arXiv  Detail & Related papers  (2025-02-06T18:52:10Z)
• Training an LLM-as-a-Judge Model: Pipeline, Insights, and Practical Lessons [9.954960702259918]
  This paper introduces Themis, a fine-tuned large language model (LLMs) judge that delivers context-aware evaluations. We provide a comprehensive overview of the development pipeline for Themis, highlighting its scenario-dependent evaluation prompts. We introduce two human-labeled benchmarks for meta-evaluation, demonstrating that Themis can achieve high alignment with human preferences in an economical manner.
  arXiv  Detail & Related papers  (2025-02-05T08:35:55Z)
• CodEv: An Automated Grading Framework Leveraging Large Language Models for Consistent and Constructive Feedback [0.0]
  This study presents an automated grading framework, CodEv, which leverages Large Language Models (LLMs) to provide consistent and constructive feedback. Our framework also integrates LLM ensembles to improve the accuracy and consistency of scores, along with agreement tests to deliver reliable feedback and code review comments.
  arXiv  Detail & Related papers  (2025-01-10T03:09:46Z)
• The Vulnerability of Language Model Benchmarks: Do They Accurately Reflect True LLM Performance? [1.3810901729134184]
  Large Language Models (LLMs) excel at standardized tests while failing to demonstrate genuine language understanding and adaptability. Our systematic analysis of NLP evaluation frameworks reveals pervasive vulnerabilities across the evaluation spectrum. We lay the groundwork for new evaluation methods that resist manipulation, minimize data contamination, and assess domain-specific tasks.
  arXiv  Detail & Related papers  (2024-12-02T20:49:21Z)
• MME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMs [97.94579295913606]
  Multimodal Large Language Models (MLLMs) have garnered increased attention from both industry and academia. In the development process, evaluation is critical since it provides intuitive feedback and guidance on improving models. This work aims to offer researchers an easy grasp of how to effectively evaluate MLLMs according to different needs and to inspire better evaluation methods.
  arXiv  Detail & Related papers  (2024-11-22T18:59:54Z)
• How Reliable are LLMs as Knowledge Bases? Re-thinking Facutality and Consistency [60.25969380388974]
  Large Language Models (LLMs) are increasingly explored as knowledge bases (KBs) Current evaluation methods focus too narrowly on knowledge retention, overlooking other crucial criteria for reliable performance. We propose new criteria and metrics to quantify factuality and consistency, leading to a final reliability score.
  arXiv  Detail & Related papers  (2024-07-18T15:20:18Z)
• DnA-Eval: Enhancing Large Language Model Evaluation through Decomposition and Aggregation [75.81096662788254]
  Large Language Models (LLMs) are scalable and economical evaluators. The question of how reliable these evaluators are has emerged as a crucial research question. We propose Decompose and Aggregate, which breaks down the evaluation process into different stages based on pedagogical practices.
  arXiv  Detail & Related papers  (2024-05-24T08:12:30Z)
• Comprehensive Reassessment of Large-Scale Evaluation Outcomes in LLMs: A Multifaceted Statistical Approach [64.42462708687921]
  Evaluations have revealed that factors such as scaling, training types, architectures and other factors profoundly impact the performance of LLMs. Our study embarks on a thorough re-examination of these LLMs, targeting the inadequacies in current evaluation methods. This includes the application of ANOVA, Tukey HSD tests, GAMM, and clustering technique.
  arXiv  Detail & Related papers  (2024-03-22T14:47:35Z)
• Evaluating Large Language Models at Evaluating Instruction Following [54.49567482594617]
  We introduce a challenging meta-evaluation benchmark, LLMBar, designed to test the ability of an LLM evaluator in discerning instruction-following outputs. We discover that different evaluators exhibit distinct performance on LLMBar and even the highest-scoring ones have substantial room for improvement.
  arXiv  Detail & Related papers  (2023-10-11T16:38:11Z)
• KoLA: Carefully Benchmarking World Knowledge of Large Language Models [87.96683299084788]
  We construct a Knowledge-oriented LLM Assessment benchmark (KoLA) We mimic human cognition to form a four-level taxonomy of knowledge-related abilities, covering $19$ tasks. We use both Wikipedia, a corpus prevalently pre-trained by LLMs, along with continuously collected emerging corpora, to evaluate the capacity to handle unseen data and evolving knowledge.
  arXiv  Detail & Related papers  (2023-06-15T17:20:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.