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The need for insights
LLMs like ChatGPT, Claude 2, Gemini, and Mistral captivate the world with their articulateness and erudition. Yet these large language models remain black boxes, concealing the intricate machinery powering their responses. Their prowess at generating human-quality text outstrips our prowess at understanding how their machine minds function.
But as artificial intelligence is set loose upon scenarios where trust and transparency are paramount, like hiring and risk assessment, explicability now moves to the fore. Explainability is no longer an optional bell or whistle on complex systems, it is an essential prerequisite to safely progressing AI in high-impact domains.
To unpack these black box models, the vibrant field of explainable NLP offers a growing toolkit — from attention visualizations revealing patterns in focus, to probing random parts of input to quantify influence. Some approaches like LIME create simplified models that mimic key decisions locally. Other methods like SHAP adapt concepts from cooperative game theory to distribute “credits” and “blame” across different parts of a model’s input based on its final output.
Regardless of technique, all pursue the same crucial end: elucidating how language models utilize the abundance of text we feed them to compose coherent passages or carry out consequential assessments.
AI already makes decisions affecting human lives — selectively judging applicants, moderating hateful content, diagnosing illness.
Explanations aren’t mere accessories — they will prove instrumental in overseeing these powerful models as they proliferate through society.
As large language models continue to advance, their inner workings remain veiled in obscurity. Yet trustworthy AI necessitates transparency into their reasoning on impactful decisions.
The vibrant field of explainable NLP offers two major approaches to elucidate model logic:
- Perturbation-based Methods: Techniques like LIME and SHAP systematically probe models by masking input components and quantify importance based on output changes. These external perspectives treat models as black boxes.
- Self-Explanations: An alternative paradigm enables models to explain their own reasoning via generated texts. For instance, highlighting pivotal input features that informed a…
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