Summary of Enhancing Trust in Llms: Algorithms For Comparing and Interpreting Llms, by Nik Bear Brown
Enhancing Trust in LLMs: Algorithms for Comparing and Interpreting LLMs
by Nik Bear Brown
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper surveys evaluation techniques to improve the trustworthiness and understanding of Large Language Models (LLMs). To ensure their reliability, fairness, and transparency, the authors explore algorithmic methods and metrics to assess LLM performance, identify weaknesses, and guide development towards more trustworthy applications. The study highlights key evaluation metrics such as 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. 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 are introduced. Human Evaluation is emphasized for capturing nuances that automated metrics may miss. The paper aims to establish a framework for evaluating LLMs, enhancing transparency, guiding development, and establishing user trust. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make sure Large Language Models (LLMs) are reliable, fair, and transparent. As people rely more on these models, it’s important to test them and see what they can do well and what they’re not good at. The authors of this paper look at different ways to evaluate LLMs, like using special metrics or testing how well they work in certain situations. They also show some new methods for evaluating LLMs, such as looking at how well they understand things on different levels or how accurate they are when they make predictions. |
Keywords
» Artificial intelligence » Bleu » Few shot » Hallucination » Machine learning » Nlp » Perplexity » Rouge » Transfer learning » Zero shot
