Retrieval or Global Context Understanding? On Many-Shot In-Context Learning for Long-Context Evaluation

• URL: http://arxiv.org/abs/2411.07130v1
• Date: Mon, 11 Nov 2024 17:00:59 GMT
• Title: Retrieval or Global Context Understanding? On Many-Shot In-Context Learning for Long-Context Evaluation
• Authors: Kaijian Zou, Muhammad Khalifa, Lu Wang,
• Abstract summary: We study long-context language models evaluation through many-shot in-context learning (ICL) We identify the skills each ICL task requires, and examine models' long-context capabilities on them. We introduce a new many-shot ICL benchmark, MANYICLBENCH, designed to characterize LCLMs' retrieval and global context understanding capabilities separately.
• Score: 10.500629810624769
• License:
• Abstract: Language models (LMs) have demonstrated an improved capacity to handle long-context information, yet existing long-context benchmarks primarily measure LMs' retrieval abilities with extended inputs, e.g., pinpointing a short phrase from long-form text. Therefore, they may fall short when evaluating models' global context understanding capacity, such as synthesizing and reasoning over content across input to generate the response. In this paper, we study long-context language model (LCLM) evaluation through many-shot in-context learning (ICL). Concretely, we identify the skills each ICL task requires, and examine models' long-context capabilities on them. We first ask: What types of ICL tasks benefit from additional demonstrations, and are these tasks effective at evaluating LCLMs? We find that classification and summarization tasks show notable performance improvements with additional demonstrations, while translation and reasoning tasks do not exhibit clear trends. This suggests the classification tasks predominantly test models' retrieval skills. Next, we ask: To what extent does each task require retrieval skills versus global context understanding from LCLMs? We develop metrics to categorize ICL tasks into two groups: (i) retrieval tasks that require strong retrieval ability to pinpoint relevant examples, and (ii) global context understanding tasks that necessitate a deeper comprehension of the full input. We find that not all datasets can effectively evaluate these long-context capabilities. To address this gap, we introduce a new many-shot ICL benchmark, MANYICLBENCH, designed to characterize LCLMs' retrieval and global context understanding capabilities separately. Benchmarking 11 open-weight LCLMs with MANYICLBENCH, we find that while state-of-the-art models perform well in retrieval tasks up to 64k tokens, many show significant drops in global context tasks at just 16k tokens.

Related papers

• Holistic Reasoning with Long-Context LMs: A Benchmark for Database Operations on Massive Textual Data [6.195658947075431]
  We introduce HoloBench, a framework that brings database reasoning operations into text-based contexts. We show that the amount of information in the context has a bigger influence on LCLM performance than the context length. We find that tasks requiring the aggregation of multiple pieces of information show a noticeable drop in accuracy as context length increases.
  arXiv  Detail & Related papers  (2024-10-15T19:04:13Z)
• DetectiveQA: Evaluating Long-Context Reasoning on Detective Novels [89.51834016940153]
  We introduce DetectiveQA, a narrative reasoning benchmark with an average context length of over 100K tokens. We use detective novels as data sources, which naturally have various reasoning elements. We manually annotated 600 questions in Chinese and then also provided an English edition of the context information and questions.
  arXiv  Detail & Related papers  (2024-09-04T06:28:22Z)
• 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)
• Can Few-shot Work in Long-Context? Recycling the Context to Generate Demonstrations [44.24067814871803]
  In-Context Learning (ICL) with few-shot examples may be an appealing solution to enhance LLM performance in long contexts. We propose to automatically generate few-shot examples for long context QA tasks by recycling contexts.
  arXiv  Detail & Related papers  (2024-06-19T15:28:29Z)
• Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More? [54.667202878390526]
  Long-context language models (LCLMs) have the potential to revolutionize our approach to tasks traditionally reliant on external tools like retrieval systems or databases. We introduce LOFT, a benchmark of real-world tasks requiring context up to millions of tokens designed to evaluate LCLMs' performance on in-context retrieval and reasoning. Our findings reveal LCLMs' surprising ability to rival state-of-the-art retrieval and RAG systems, despite never having been explicitly trained for these tasks.
  arXiv  Detail & Related papers  (2024-06-19T00:28:58Z)
• Does In-Context Learning Really Learn? Rethinking How Large Language Models Respond and Solve Tasks via In-Context Learning [41.606494950216764]
  In-context Learning (ICL) has emerged as a powerful capability alongside the development of scaled-up large language models (LLMs) This paper decomposes the overall performance of ICL into three dimensions, label space, format, and discrimination. We show that ICL exhibits significant efficacy in regulating the label space and format, which helps LLMs respond to desired label words.
  arXiv  Detail & Related papers  (2024-04-11T08:20:10Z)
• CODIS: Benchmarking Context-Dependent Visual Comprehension for Multimodal Large Language Models [58.95889895912716]
  We introduce a new benchmark, named as CODIS, designed to assess the ability of models to use context provided in free-form text to enhance visual comprehension. Our findings indicate that MLLMs consistently fall short of human performance on this benchmark. This underscores the pressing need to enhance the ability of MLLMs to comprehend visuals in a context-dependent manner.
  arXiv  Detail & Related papers  (2024-02-21T08:21:12Z)
• LMRL Gym: Benchmarks for Multi-Turn Reinforcement Learning with Language Models [56.25156596019168]
  This paper introduces the LMRL-Gym benchmark for evaluating multi-turn RL for large language models (LLMs) Our benchmark consists of 8 different language tasks, which require multiple rounds of language interaction and cover a range of tasks in open-ended dialogue and text games.
  arXiv  Detail & Related papers  (2023-11-30T03:59:31Z)
• Can Large Language Models Understand Real-World Complex Instructions? [54.86632921036983]
  Large language models (LLMs) can understand human instructions, but struggle with complex instructions. Existing benchmarks are insufficient to assess LLMs' ability to understand complex instructions. We propose CELLO, a benchmark for evaluating LLMs' ability to follow complex instructions systematically.
  arXiv  Detail & Related papers  (2023-09-17T04:18:39Z)
• ICL-D3IE: In-Context Learning with Diverse Demonstrations Updating for Document Information Extraction [56.790794611002106]
  Large language models (LLMs) have demonstrated remarkable results in various natural language processing (NLP) tasks with in-context learning. We propose a simple but effective in-context learning framework called ICL-D3IE. Specifically, we extract the most difficult and distinct segments from hard training documents as hard demonstrations.
  arXiv  Detail & Related papers  (2023-03-09T06:24:50Z)

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.