Update README.md

README.md

@@ -10,7 +10,7 @@ pretty_name: NoLiMa
 # NoLiMa: Long-Context Evaluation Beyond Literal Matching
 
 ## Abstract
-> Recent large language models (LLMs) support long contexts ranging from 128K to 1M tokens. A popular method for evaluating these capabilities is the needle-in-a-haystack (NIAH) test, which involves retrieving a "needle" (relevant information) from a "haystack" (long irrelevant context). Extensions of this approach include increasing distractors, fact chaining, and in-context reasoning. However, in these benchmarks, models can exploit existing literal matches between the needle and haystack to simplify the task. To address this, we introduce **NoLiMa**, a benchmark extending NIAH with a carefully designed needle set, where questions and needles have **minimal lexical overlap, requiring models to infer latent associations to locate the needle within the haystack**. We evaluate 12 popular LLMs that claim to support contexts of at least 128K tokens. While they perform well in short contexts (<1K), performance degrades significantly as context length increases. At 32K, for instance, 10 models drop below 50\% of their strong short-length baselines. Even GPT-4o, one of the top-performing exceptions, experiences a reduction from an almost-perfect baseline of 99.3\% to 69.7\%. Our analysis suggests these declines stem from the increased difficulty the attention mechanism faces in longer contexts when literal matches are absent, making it harder to retrieve relevant information.
+Recent large language models (LLMs) support long contexts ranging from 128K to 1M tokens. A popular method for evaluating these capabilities is the needle-in-a-haystack (NIAH) test, which involves retrieving a "needle" (relevant information) from a "haystack" (long irrelevant context). Extensions of this approach include increasing distractors, fact chaining, and in-context reasoning. However, in these benchmarks, models can exploit existing literal matches between the needle and haystack to simplify the task. To address this, we introduce **NoLiMa**, a benchmark extending NIAH with a carefully designed needle set, where questions and needles have **minimal lexical overlap, requiring models to infer latent associations to locate the needle within the haystack**. We evaluate 12 popular LLMs that claim to support contexts of at least 128K tokens. While they perform well in short contexts (<1K), performance degrades significantly as context length increases. At 32K, for instance, 10 models drop below 50\% of their strong short-length baselines. Even GPT-4o, one of the top-performing exceptions, experiences a reduction from an almost-perfect baseline of 99.3\% to 69.7\%. Our analysis suggests these declines stem from the increased difficulty the attention mechanism faces in longer contexts when literal matches are absent, making it harder to retrieve relevant information.
 
 ## Results
 | Models               | Claimed Length | Effective Length | Base Score<br>(×0.85: Thr.) | 1K  | 2K  | 4K  | 8K  | 16K | 32K |