Evaluating Long-Context Language Models' Resistance to Noisy Contexts
This article discusses the evaluation of long-context language models (LCLMs), focusing on their ability to handle noisy or corrupted long-context inputs.
Why it matters
Evaluating LCLM robustness to noisy contexts is crucial for real-world applications where models may encounter unreliable or irrelevant information.
Key Points
- 1Evaluation of LCLM capabilities: long-context comprehension and long-form generation
- 2Synthetic vs. real-world evaluation tasks for LCLMs
- 3Key LCLM abilities: retrieval, aggregation, and reasoning from long contexts
Details
The article discusses different approaches to evaluating LCLM performance, including synthetic tasks like Needle-in-a-Haystack (NIAH) and more real-world evaluation tasks like LongBench v2. Synthetic tasks allow for better control over input length and ground truth, while real-world tasks provide more realistic and consistent input contexts. The article also outlines three key LCLM abilities: retrieval (finding relevant information in long contexts), aggregation (combining information from long contexts), and reasoning (drawing logical conclusions from long contexts). The focus of this work is evaluating how well LCLMs can resist the influence of noisy or corrupted information in long input contexts.
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