What is the context size in NLP?
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"In natural language processing (NLP), the context size plays a crucial role in determining the performance of various tasks. Different studies, such as those by Herold & Ney, Hung et al., Curran & Moens, and Dörpinghaus et al., have highlighted the importance of context windows and the trade-offs involved. While some research focuses on determining the optimal context size for specific linguistic phenomena, others explore the impact of context quantity, quality, and extraction methods on NLP systems. The context size varies depending on the task at hand, with considerations for syntactic and semantic functions, memory consumption, and the balance between local and document-level context ."
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| The context size in NLP varies, but in document-level NMT, increasing context beyond local sentences can improve translation quality while facing challenges like memory usage and performance degradation. | |
5 Citations | The context window size for Chinese text was determined to be 9 characters, balancing task performance and context size in natural language processing. |
| The context size in NLP refers to the quantity of contextual information available to describe each term, impacting system accuracy. | |
| The context size in NLP refers to the amount of surrounding text used for understanding a specific word or phrase. | |
Open access•Posted Content | The context size in NLP is crucial for understanding natural language, as shown in the proposed large-scale biomedical knowledge graph approach. |
Related Questions
What's context in language ambiguity?5 answersContext in language ambiguity refers to the surrounding information that helps determine the meaning of ambiguous words or phrases. Natural language often contains ambiguity, which can be advantageous due to context dependence. Ambiguity in language can lead to enhanced memory for items in the referential context, especially during temporary ambiguity in spoken language. Studies show that contextualized language models can distinguish between different meanings of ambiguous words based on their contexts, with polysemic interpretations falling on a continuum between identical meanings and homonymy. Additionally, research indicates that polysemous words, with multiple related senses, have a processing advantage compared to unambiguous words, while homonymous words, with multiple unrelated meanings, have a processing disadvantage. This demonstrates the importance of context in disambiguating language.
What is Context, and how can be defined for Artificial Intelligence models reuse?5 answersContext in the realm of Artificial Intelligence (AI) models refers to the surrounding information and conditions that influence the model's development and application. Defining context for AI model reuse involves capturing optimization curves during model training to characterize the model based on metrics, aiding in accuracy refinement, hyper-parameter sensitivity understanding, and multi-objective model reuse. The complexity lies in understanding the relationships between trained AI models and optimization curves, validating quantitative metrics, and disseminating these metrics effectively. By utilizing context elements expressed in i* and ontology, the reuse of context models can be facilitated, supporting the construction of new systems. Overall, defining context for AI model reuse involves considering various factors such as decision, project, and enterprise contexts to enhance project management support.
How effective are different techniques for increasing context size in LLMas compared to each other?4 answersTo increase the context size in Large Language Models (LLMs), various techniques have been proposed in recent research. Position Interpolation (PI) is one such method that extends the context window sizes of RoPE-based pretrained LLMs up to 32768 with minimal fine-tuning, demonstrating strong empirical results on tasks requiring long context. Another approach, Selective Context, filters out less informative content using self-information to enhance the efficiency of fixed context length, showing effectiveness in tasks like summarization and question answering. In contrast, techniques focusing on enhancing LLMs' contextual faithfulness, such as opinion-based prompts and counterfactual demonstrations, have been shown to significantly improve faithfulness to contexts in tasks like machine reading comprehension and relation extraction.
Parallel Context Windows (PCW) is a method that alleviates the context window restriction for off-the-shelf LLMs without further training by dividing long contexts into chunks and restricting attention mechanisms within each window, showing substantial improvements for tasks with diverse input and output spaces. On the other hand, the In-context Autoencoder (ICAE) compresses long context into memory slots, enabling the target LLM to respond effectively to various prompts, showcasing a $4\times$ context compression with promising results for reducing computation and memory overheads in LLM inference.
Comparing these techniques, Position Interpolation and PCW focus on extending context size directly, while Selective Context and techniques enhancing contextual faithfulness aim to improve the quality of information within the existing context window. Both approaches have shown effectiveness in different tasks, with Position Interpolation and PCW demonstrating success in handling longer contexts efficiently, and Selective Context and faithfulness enhancement techniques improving the quality of responses within the existing context limits. The choice of technique may depend on the specific task requirements and the trade-off between context size and information quality that needs to be addressed.
What are the potential drawbacks of increasing the context size of LLMs?5 answersIncreasing the context size of Large Language Models (LLMs) can lead to the models becoming "lazy learners" that rely on shortcuts or spurious correlations within prompts, especially during inference. Additionally, larger models are more likely to exploit shortcuts in prompts for downstream tasks. This reliance on shortcuts can affect the robustness of in-context learning and pose challenges for detecting and mitigating the misuse of prompts by LLMs.
What techniques can be used to increase the context size of language models?5 answersSeveral techniques have been proposed to increase the context size of language models (LMs), addressing the limitations imposed by finite context windows. One approach is Position Interpolation (PI), which extends the context window sizes of RoPE-based pretrained LLMs, such as LLaMA models, to up to 32768 with minimal fine-tuning, demonstrating strong empirical results on tasks requiring long context. Another technique, In-Context Retrieval-Augmented Language Modeling (RALM), leaves the LM architecture unchanged and prepends grounding documents to the input, leveraging off-the-shelf general-purpose retrievers to provide significant LM gains across model sizes and diverse corpora. AutoCompressors adapt pre-trained LMs to compress long contexts into compact summary vectors, which are then accessible as soft prompts, effectively extending the context window while speeding up inference over long contexts. The Language Models Augmented with Long-Term Memory (LongMem) framework enables LLMs to memorize long history through a novel decoupled network architecture, allowing for the handling of unlimited-length context in its memory bank. Lastly, Parallel Context Windows (PCW) is a method that alleviates the context window restriction for any off-the-shelf LLM without further training by carving a long context into chunks and restricting the attention mechanism to apply only within each window. These techniques collectively offer promising solutions for extending the context window of LMs, enabling them to process longer text sequences effectively.
What is Context?5 answersContext is an entirety that arises from interaction and communication between people and objects mutually connected in networks. It includes the circumstances, background information, and settings that surround a situation or event and help to give it meaning. In the context of language and communication, it refers to the time, place, people involved, cultural and social norms, and larger issues and events that may be related. In the context of computing and technology, it refers to the information and settings that are relevant to a specific task or application. In architectural design, context refers to the physical architectural objects nearby, the interacting natural and man-made environment, and the constantly developing cultural, social, and economic processes. The concept of context is explored from various scientific perspectives, emphasizing the exchange of information and shared experiences between people and intelligent objects or cyber-physical systems.