SLANG: New Concept Comprehension of Large Language Models

• URL: http://arxiv.org/abs/2401.12585v5
• Date: Fri, 04 Oct 2024 13:03:49 GMT
• Title: SLANG: New Concept Comprehension of Large Language Models
• Authors: Lingrui Mei, Shenghua Liu, Yiwei Wang, Baolong Bi, Xueqi Cheng,
• Abstract summary: Large language models (LLMs) often struggle to keep up with the rapid linguistic evolution characteristic of online communities. Our benchmark and approach involves understanding real-world instances of linguistic shifts, serving as contextual beacons. Our causal inference-based approach outperforms the baseline methods in terms of precision and relevance in the comprehension of Internet slang and memes.
• Score: 43.486543309365416
• License:
• Abstract: The dynamic nature of language, particularly evident in the realm of slang and memes on the Internet, poses serious challenges to the adaptability of large language models (LLMs). Traditionally anchored to static datasets, these models often struggle to keep up with the rapid linguistic evolution characteristic of online communities. This research aims to bridge this gap by enhancing LLMs' comprehension of the evolving new concepts on the Internet, without the high cost of continual retraining. In pursuit of this goal, we introduce $\textbf{SLANG}$, a benchmark designed to autonomously integrate novel data and assess LLMs' ability to comprehend emerging concepts, alongside $\textbf{FOCUS}$, an approach uses causal inference to enhance LLMs to understand new phrases and their colloquial context. Our benchmark and approach involves understanding real-world instances of linguistic shifts, serving as contextual beacons, to form more precise and contextually relevant connections between newly emerging expressions and their meanings. The empirical analysis shows that our causal inference-based approach outperforms the baseline methods in terms of precision and relevance in the comprehension of Internet slang and memes.

Related papers

• Improving Large Language Model (LLM) fidelity through context-aware grounding: A systematic approach to reliability and veracity [0.0]
  Large Language Models (LLMs) are increasingly sophisticated and ubiquitous in natural language processing (NLP) applications. This paper presents a novel framework for contextual grounding in textual models, with a particular emphasis on the Context Representation stage. Our findings have significant implications for the deployment of LLMs in sensitive domains such as healthcare, legal systems, and social services.
  arXiv  Detail & Related papers  (2024-08-07T18:12:02Z)
• Semantic Change Characterization with LLMs using Rhetorics [0.1474723404975345]
  We investigate the potential of LLMs in characterizing three types of semantic change: thought, relation, and orientation. Our results highlight the effectiveness of LLMs in capturing and analyzing semantic changes, providing valuable insights to improve computational linguistic applications.
  arXiv  Detail & Related papers  (2024-07-23T16:32:49Z)
• Text-Video Retrieval with Global-Local Semantic Consistent Learning [122.15339128463715]
  We propose a simple yet effective method, Global-Local Semantic Consistent Learning (GLSCL) GLSCL capitalizes on latent shared semantics across modalities for text-video retrieval. Our method achieves comparable performance with SOTA as well as being nearly 220 times faster in terms of computational cost.
  arXiv  Detail & Related papers  (2024-05-21T11:59:36Z)
• CoLLEGe: Concept Embedding Generation for Large Language Models [12.812113254812028]
  CoLLEGe is a meta-learning framework capable of generating flexible embeddings for new concepts. We design a series of tasks to test new concept learning in challenging real-world scenarios.
  arXiv  Detail & Related papers  (2024-03-22T17:26:05Z)
• Sparsity-Guided Holistic Explanation for LLMs with Interpretable Inference-Time Intervention [53.896974148579346]
  Large Language Models (LLMs) have achieved unprecedented breakthroughs in various natural language processing domains. The enigmatic black-box'' nature of LLMs remains a significant challenge for interpretability, hampering transparent and accountable applications. We propose a novel methodology anchored in sparsity-guided techniques, aiming to provide a holistic interpretation of LLMs.
  arXiv  Detail & Related papers  (2023-12-22T19:55:58Z)
• MAGNIFICo: Evaluating the In-Context Learning Ability of Large Language Models to Generalize to Novel Interpretations [37.13707912132472]
  Humans possess a remarkable ability to assign novel interpretations to linguistic expressions. Large Language Models (LLMs) have a knowledge cutoff and are costly to finetune repeatedly. We systematically analyse the ability of LLMs to acquire novel interpretations using in-context learning.
  arXiv  Detail & Related papers  (2023-10-18T00:02:38Z)
• Large Language Models for Information Retrieval: A Survey [58.30439850203101]
  Information retrieval has evolved from term-based methods to its integration with advanced neural models. Recent research has sought to leverage large language models (LLMs) to improve IR systems. We delve into the confluence of LLMs and IR systems, including crucial aspects such as query rewriters, retrievers, rerankers, and readers.
  arXiv  Detail & Related papers  (2023-08-14T12:47:22Z)
• Commonsense Knowledge Transfer for Pre-trained Language Models [83.01121484432801]
  We introduce commonsense knowledge transfer, a framework to transfer the commonsense knowledge stored in a neural commonsense knowledge model to a general-purpose pre-trained language model. It first exploits general texts to form queries for extracting commonsense knowledge from the neural commonsense knowledge model. It then refines the language model with two self-supervised objectives: commonsense mask infilling and commonsense relation prediction.
  arXiv  Detail & Related papers  (2023-06-04T15:44:51Z)
• Continual Learning for Natural Language Generation in Task-oriented Dialog Systems [72.92029584113676]
  Natural language generation (NLG) is an essential component of task-oriented dialog systems. We study NLG in a "continual learning" setting to expand its knowledge to new domains or functionalities incrementally. The major challenge towards this goal is catastrophic forgetting, meaning that a continually trained model tends to forget the knowledge it has learned before.
  arXiv  Detail & Related papers  (2020-10-02T10:32:29Z)

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.