Natural Language Processing
Processing and understanding text? Evaluate your models on language understanding, generation, translation, and information extraction benchmarks.
NLP in 2025 has matured from research systems to production infrastructure. Frontier models reach 95% on MMLU while solving alignment problems through Constitutional AI. The field now balances raw capability with practical deployment concerns.
State of the Field (Dec 2024)
- -Frontier models (GPT-5.1, Claude 3.5, DeepSeek-V3) achieve 88-92% on MMLU with context windows expanding to 2M tokens, though long-context reasoning still degrades with scale
- -Open-source models now match proprietary performance - DeepSeek-V3 (88.5 MMLU) rivals GPT-4o while Llama 4 offers 30x cost reduction at respectable capability
- -RAG adopted in 78% of production systems as standard architecture, while agentic AI enables multi-step autonomous task completion with 30% of orgs exploring deployment
- -Constitutional AI reduced harmful outputs by 85% vs 2023, but hallucination remains critical challenge requiring explicit mitigation strategies in production
Quick Recommendations
General-purpose tasks requiring frontier capability
GPT-5.1 or Claude 3.5 Sonnet
Most consistent performance across benchmarks (92% MMLU for GPT-5.1, 88.9% for Claude). Battle-tested production infrastructure and SLAs justify premium cost for critical applications.
Cost-sensitive deployments with data privacy needs
DeepSeek-V3 or Llama 4 Scout
DeepSeek-V3 matches GPT-4o performance (88.5 MMLU) with local deployment. Llama 4 costs 30x less ($0.1/1M tokens) with 86% MMLU - exceptional value for budget constraints.
Machine translation for production
Claude 3.5 Sonnet for general, DeepL for critical content
Claude achieved 78% professional 'good' ratings, highest among LLMs. DeepL hybrid approach requires 2-3x fewer corrections for publication-ready translation despite narrower language coverage.
Asian language processing and multimodal tasks
Qwen 3 (72B) or Qwen3-VL (235B)
Maintains 95% terminology accuracy for Asian technical content. Qwen3-VL rivals GPT-V on vision benchmarks with superior 20x visual compression at 97% OCR accuracy.
Edge deployment and resource-constrained environments
DistilBERT, Mistral-7B, or quantized Llama 3
DistilBERT retains 97% GLUE performance at 40% parameter reduction. Quantized Llama 3 (2-4 bit) runs on mobile devices while handling straightforward QA and dialogue effectively.
Enterprise knowledge management with proprietary data
RAG architecture with Claude/GPT-5 + semantic search
78% of production systems use RAG for good reason - enables knowledge updates without retraining, stronger hallucination mitigation through grounding, and maintains data privacy.
Complex reasoning requiring step-by-step verification
Test-time compute scaling with o1 or similar
Allocating compute during inference enables smaller models to outperform 14x larger models on complex problems. 4x efficiency improvement through compute-optimal allocation strategies.
Multi-step autonomous task execution
Agentic frameworks with GPT-5 or Claude as orchestrator
Agentic AI handles complex workflows requiring planning, execution, and iteration. Capital One achieved 5x latency reduction, Salesforce closed 18k deals since October 2024 launch.
Tasks & Benchmarks
Language Modeling
Predicting the next word or token in a sequence. Core task for GPT-style models.
Machine Translation
Translating text from one language to another (WMT benchmarks).
Named Entity Recognition
Identifying and classifying named entities in text (CoNLL).
Natural Language Inference
Determining entailment relationships between sentences (SNLI, MNLI).
Question Answering
Answering questions based on context (SQuAD, Natural Questions).
Reading Comprehension
Understanding and answering questions about passages.
Semantic Textual Similarity
Measuring similarity between text pairs (STS Benchmark).
Text Classification
Categorizing text into predefined classes (sentiment, topic).
Text Summarization
Generating concise summaries of longer documents (CNN/DailyMail, XSum).
Show all datasets and SOTA results
Language Modeling
Machine Translation
Named Entity Recognition
Reuters news stories annotated with 4 entity types: PER, ORG, LOC, MISC. The standard NER benchmark.
Natural Language Inference
570k human-written English sentence pairs manually labeled for balanced classification with the labels entailment, contradiction, and neutral.
Question Answering
150K questions on Wikipedia articles, including 50K unanswerable questions. Tests reading comprehension and knowing when a question cannot be answered.
Reading Comprehension
Semantic Textual Similarity
Text Classification
Collection of 9 NLU tasks including sentiment analysis, textual entailment, and question answering. Standard benchmark for general language understanding.
More difficult successor to GLUE with 8 challenging tasks. Designed to be hard for current models.
Text Summarization
300K news articles with multi-sentence summaries. Standard benchmark for abstractive summarization.
Honest Takes
Benchmark scores are mostly contamination
Models perform substantially better on problems released before their training cutoff than after. GLUE and SuperGLUE are saturated. Focus on domain-specific evaluation on your actual production data, not leaderboard rankings.
Longer context doesn't mean better reasoning
Models now support 2M token windows but reasoning degrades even when retrieval succeeds. Don't assume more context improves performance - test carefully as adding documents can hurt accuracy.
RAG beats fine-tuning for most use cases
Unless you have domain-specific style requirements or truly unique knowledge patterns, RAG provides better ROI. Knowledge updates without retraining, lower cost, and retrieval quality matters more than model size.
Edge deployment is viable now
90% size reduction through quantization while maintaining 95%+ accuracy enables capable models on mobile devices. Edge deployment grew 340% in 2025 - privacy and latency benefits outweigh cloud convenience for many applications.
Specialized models still win on translation
While LLMs won 9 of 11 WMT24 language pairs, hybrid approaches like DeepL require 2-3x fewer editorial corrections. For production translation at scale, domain-specific models justify the added complexity.