Long-context LLMs Struggle with Long In-context Learning

• URL: http://arxiv.org/abs/2404.02060v3
• Date: Wed, 12 Jun 2024 02:46:16 GMT
• Title: Long-context LLMs Struggle with Long In-context Learning
• Authors: Tianle Li, Ge Zhang, Quy Duc Do, Xiang Yue, Wenhu Chen,
• Abstract summary: We introduce a benchmark for long in-context learning in extreme-label classification using six datasets with 28 to 174 classes and input lengths from 2K to 50K tokens. We evaluate on 15 long-context LLMs and find that they perform well on less challenging classification tasks with smaller label space and shorter demonstrations. Further analysis reveals a bias towards labels presented later in the sequence and a need for improved reasoning over multiple pieces of information.
• Score: 39.84597097614607
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) have made significant strides in handling long sequences. Some models like Gemini could even to be capable of dealing with millions of tokens. However, their performance evaluation has largely been confined to metrics like perplexity and synthetic tasks, which may not fully capture their true abilities in more challenging, real-world scenarios. We introduce a benchmark (LongICLBench) for long in-context learning in extreme-label classification using six datasets with 28 to 174 classes and input lengths from 2K to 50K tokens. Our benchmark requires LLMs to comprehend the entire input to recognize the massive label spaces to make correct predictions. We evaluate on 15 long-context LLMs and find that they perform well on less challenging classification tasks with smaller label space and shorter demonstrations. However, they struggle with more challenging task like Discovery with 174 labels, suggesting a gap in their ability to process long, context-rich sequences. Further analysis reveals a bias towards labels presented later in the sequence and a need for improved reasoning over multiple pieces of information. Our study reveals that long context understanding and reasoning is still a challenging task for the existing LLMs. We believe LongICLBench could serve as a more realistic evaluation for the future long-context LLMs.

Related papers

• LIFEBench: Evaluating Length Instruction Following in Large Language Models [41.637005190608946]
  We introduce LIFEBench to evaluate large language models' ability to follow length instructions.<n>LIFEBench consists of 10,800 instances across 4 task categories in both English and Chinese.<n>We find that most models reasonably follow short-length instructions but deteriorate sharply beyond a certain threshold.
  arXiv  Detail & Related papers  (2025-05-22T05:08:27Z)
• SCoRE: Benchmarking Long-Chain Reasoning in Commonsense Scenarios [33.72114830484246]
  We introduce SCoRE (Scenario-based Commonsense Reasoning Evaluation), a benchmark that synthesizes multi-hop questions from scenario schemas of entities, relations, and logical rules.<n>SCoRE contains 100k bilingual (Chinese-English) multiple-choice questions whose reasoning chains span 2-11 hops and are grouped into various difficulty levels.
  arXiv  Detail & Related papers  (2025-03-08T13:40:10Z)
• Zero-to-Strong Generalization: Eliciting Strong Capabilities of Large Language Models Iteratively without Gold Labels [75.77877889764073]
  Large Language Models (LLMs) have demonstrated remarkable performance through supervised fine-tuning or in-context learning using gold labels. This study explores whether solely utilizing unlabeled data can elicit strong model capabilities. We propose a new paradigm termed zero-to-strong generalization.
  arXiv  Detail & Related papers  (2024-09-19T02:59:44Z)
• NeedleBench: Can LLMs Do Retrieval and Reasoning in 1 Million Context Window? [37.64593022203498]
  NeedleBench is a framework consisting of progressively more challenging tasks for assessing bilingual long-context capabilities. We use the framework to assess how well the leading open-source models can identify key information relevant to the question. We propose the Ancestral Trace Challenge to mimic the complexity of logical reasoning challenges that are likely to be present in real-world long-context tasks.
  arXiv  Detail & Related papers  (2024-07-16T17:59:06Z)
• Prompting Large Language Models with Knowledge Graphs for Question Answering Involving Long-tail Facts [50.06633829833144]
  Large Language Models (LLMs) are effective in performing various NLP tasks, but struggle to handle tasks that require extensive, real-world knowledge. We propose a benchmark that requires knowledge of long-tail facts for answering the involved questions. Our experiments show that LLMs alone struggle with answering these questions, especially when the long-tail level is high or rich knowledge is required.
  arXiv  Detail & Related papers  (2024-05-10T15:10:20Z)
• Ada-LEval: Evaluating long-context LLMs with length-adaptable benchmarks [76.43527940649939]
  We introduce Ada-LEval, a benchmark for evaluating the long-context understanding of large language models (LLMs) Ada-LEval includes two challenging subsets, TSort and BestAnswer, which enable a more reliable evaluation of LLMs' long context capabilities. We evaluate 4 state-of-the-art closed-source API models and 6 open-source models with Ada-LEval.
  arXiv  Detail & Related papers  (2024-04-09T17:30:48Z)
• XL$^2$Bench: A Benchmark for Extremely Long Context Understanding with Long-range Dependencies [45.31042312867939]
  Large Language Models (LLMs) have demonstrated remarkable performance across diverse tasks but are constrained by their small context window sizes. Various efforts have been proposed to expand the context window to accommodate even up to 200K input tokens. We introduce a benchmark for extremely long context understanding with long-range dependencies, XL$2$Bench.
  arXiv  Detail & Related papers  (2024-04-08T12:29:07Z)
• NovelQA: Benchmarking Question Answering on Documents Exceeding 200K Tokens [63.7488938083696]
  NovelQA is a benchmark designed to test the capabilities of Large Language Models with extended texts. This paper presents the design and construction of NovelQA, highlighting its manual annotation, and diverse question types. Our evaluation of Long-context LLMs on NovelQA reveals significant insights into the models' performance.
  arXiv  Detail & Related papers  (2024-03-18T17:32:32Z)
• LooGLE: Can Long-Context Language Models Understand Long Contexts? [46.143956498529796]
  LooGLE is a benchmark for large language models' long context understanding. It features relatively new documents post-2022, with over 24,000 tokens per document and 6,000 newly generated questions spanning diverse domains. The evaluation of eight state-of-the-art LLMs on LooGLE revealed key findings.
  arXiv  Detail & Related papers  (2023-11-08T01:45:37Z)
• TRACE: A Comprehensive Benchmark for Continual Learning in Large Language Models [52.734140807634624]
  Aligned large language models (LLMs) demonstrate exceptional capabilities in task-solving, following instructions, and ensuring safety. Existing continual learning benchmarks lack sufficient challenge for leading aligned LLMs. We introduce TRACE, a novel benchmark designed to evaluate continual learning in LLMs.
  arXiv  Detail & Related papers  (2023-10-10T16:38:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.