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SLANG: New Concept Comprehension of Large Language Models

Lingrui Mei, Shenghua Liu, Yiwei Wang, Baolong Bi, Xueqi Cheng

arXiv:2401.12585v6cs.CL

TL;DR

Online language evolves rapidly, while LLMs trained on static data struggle to understand new concepts. The paper introduces SLANG to evaluate emerging concepts and FOCUS to apply causal inference to their interpretation; with Claude 3, FOCUS reports an F1 score of 0.4596 and outperforms previous methods.

  • Problem

    Rapid online language change challenges LLMs trained on static data, which may struggle to adapt to emerging concepts without constant updates or external data.

    Source: §1 · p. 1

  • Method

    The paper introduces SLANG, a benchmark for linguistic shifts, and FOCUS, a causal-inference approach to comprehension of new concepts.

    Source: §1 · p. 2

  • Results

    With Claude 3, FOCUS achieved an F1 score of 0.4596, precision of 0.4452, recall of 0.4827, and accuracy of 89.7%, outperforming previous methods.

    Source: §1 · p. 2

  • Takeaways & Limitations

    The study concludes that causal inference can improve understanding of emerging linguistic phenomena, with FOCUS going beyond other methods in precision and relevance.

    Source: §6 · p. 8

    The work does not fully address non-English or morphologically rich languages, and FOCUS’s higher computational complexity may delay mobile deployment.

    Source: Limitations · p. 9

Abstract

from arXiv · show

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.

1 Introduction

Rapid online language change challenges LLMs trained on static data, and literal pattern-based interpretations can miss metaphorical meaning. The paper introduces SLANG to assess adaptability and FOCUS to improve comprehension through causal inference, reporting stronger results than previous methods.

1 Introduction

  • Online language evolves rapidly, while LLMs trained on static data struggle to adapt to newly emerging concepts without constant updates or external data.Source: §1 · p. 1
  • CoT can interpret a phrase literally while missing its metaphorical meaning, such as a sunset remark signaling the end of a life phase or relationship.Source: §1 · pp. 1–2
  • SLANG assesses LLM adaptability to linguistic shifts, while FOCUS applies causal inference to improve comprehension of new concepts.Source: §1 · p. 2
  • SLANG draws on recent UrbanDictionary entries, filters phrases likely to appear in training data, and uses user ratings to refine its dataset.Source: §1 · p. 2
  • With Claude 3, FOCUS achieved an F1 score of 0.4596, precision of 0.4452, recall of 0.4827, and accuracy of 89.7%, outperforming previous methods.The authors report these results for comprehension and adaptability.Source: §1 · p. 2

2 SLANG

SLANG evaluates how well LLMs interpret new user-generated concepts using factual and counterfactual data. Its construction filters and standardizes recent UrbanDictionary entries, while its metrics accommodate synonymous interpretations.

2 SLANG

  • SLANG evaluates LLMs’ interpretation of dynamic user-generated concepts using both factual and counterfactual datasets.The paper presents dataset construction and evaluation metrics as components of the benchmark.Source: §2 · p. 2

2.1 Preprocessing

  • The pipeline extracts recent UrbanDictionary phrases, definitions, examples, and ratings, and is designed to add fresh data for future cutoff dates.Source: §2.1 · p. 2
  • Temporal and novelty checks, vote filtering, and removal of inappropriate content reduced 7,220 concepts to 408 usable new concepts.The sequential filtering steps removed 5,463, 1,328, and 21 samples.Source: §2.1 · pp. 2–3

2.2 Metrics

  • Explanations are standardized into dictionary format, and evaluation combines lexical metrics with sentence similarity, treating SimCSE scores above 0.7 as positive for accuracy.For each entry, the final metric uses the interpretation with the highest BLEU score among five generated variants.Source: §2.1 · p. 3, §2.2 · pp. 3–4

2.1 Preprocessing

  • The counterfactual dataset preserves original phrases while replacing entities and generating new contexts and explanations with GPT-4.The resulting entries are intended to convey meanings distinct from the factual dataset.Source: §2.1 · p. 3

3 FOCUS

FOCUS models phrase interpretation as a causal process involving context, phrase, entities, and linguistic factors. Its interventions mask or replace entities and synthesize multiple interpretations to focus analysis on contextual meaning.

3 FOCUS

  • FOCUS frames interpretation as an LLM output Y produced from phrase W and context X, with a simplified SCM mapping both inputs through model M.Source: §3.1 · p. 4, §3 · p. 4

3.1 Causal Analysis

  • The causal analysis treats entities E as context-derived confounders and linguistic factors R as shaping context, while considering their influence on interpretation.The account includes direct paths from context X and phrase W to interpretation Y, as well as mediated paths through entities.Source: §3.1 · pp. 4–5

3.2 Context-based Causal Intervention

  • FOCUS uses causal intervention to modify entities and context, then replaces phrase W with a placeholder to reduce reliance on entity-specific bias.These adjustments are intended to emphasize the role of linguistic factors in shaping context.Source: §3.2 · p. 5
  • The four-stage method begins with Direct Inquiry, which provides a baseline interpretation from the usage context and phrase.The broader method aims to analyze usage examples and generate counterfactual interpretations.Source: §3.2 · pp. 5–6, Figure 5 · p. 6
  • Masked Entity Inquiry masks the phrase to elicit context-based synonyms, while Entity Replacement Inquiry alters context entities to examine interpretation variability.Source: §3.2 · p. 6
  • Synthesis combines Direct Inquiry, Masked Entity Inquiry, and Entity Replacement Inquiry to reconcile their interpretations and confounders.Source: §3.2 · p. 6

4 Experiments

Experiments evaluate FOCUS against several baselines on factual and counterfactual datasets, then ablate its MEI and ERI components. FOCUS reports strong results across the tested models, while removing either component lowers GPT-4 factual-dataset F1.

4.1 Setup

  • Evaluations used 408 new concepts filtered from an initial dataset of 7,220 and compared direct inquiry, Chain-of-Thought, CauView, and FOCUS.The models included Claude 3, GPT-4, and Mistral-7B.Source: §4.1 · p. 6

4.3 Experimental Results

  • FOCUS reports factual-dataset F1 scores of 0.4446 for GPT-4, 0.4596 for Claude 3, and 0.3703 for Mistral 7B; counterfactual-dataset F1 scores are 0.4532, 0.4636, and 0.3935, respectively.The factual results also report GPT-4 precision 0.4280, recall 0.4714, and accuracy 88.2%; Claude 3 accuracy 89.7%; and Mistral 7B accuracy 76.0%. Counterfactual results report GPT-4 precision 0.4598, recall 0.4551, and accuracy 84.9%; Claude 3 accuracy 86.8%; and Mistral 7B accuracy 77.5%.Source: §4.3 · p. 7, Table 1 · p. 7

4.4 Ablation Study

  • Excluding MEI reduced F1 from 0.4446 to 0.4366; the result illustrates MEA’s role in disentangling the direct influence of phrase W and context X from confounding entities E.MEI is represented as P(Y | W, X, E; M), where Y is the interpretation, W the phrase, X the context, and E the entities.Source: §4.4 · p. 7
  • Removing ERI lowered GPT-4 factual-dataset F1 to 0.4283, and the study describes combined MEI and ERI as necessary for optimal results.ERI examines the causal links X → Y ← E and X → E, isolating entity influence and alternative causal pathways.Source: §4.4 · p. 7

5 Related Work

Related work covers approaches to updating model knowledge, biases and shortcuts in language processing, and causal interventions. It notes trade-offs in updating and retrieval methods, and limits in prior causal approaches.

5.1 Knowledge Update Methods

  • Parameter-efficient fine-tuning methods include task-specific parameter addition [Houlsby et al., 2019] and LoRA, while BitFit simplifies fine-tuning but depends heavily on dataset quality.These approaches face computational demands, catastrophic forgetting, or reduced task-specific effectiveness; retrieval-augmented generation and in-context knowledge editing focus more on fact retrieval than deeper understanding.Source: §5.1 · pp. 7–8

5.2 Entity Bias and Shortcuts in LLMs

  • Entity bias and shortcuts can produce oversimplified processing based on particular entities or dataset patterns, limiting nuanced language understanding, generalization, and robustness [Peng et al., 2020; Longpre et al., 2021; Wang et al., 2022, 2023; Du et al., 2021; Saparov and He, 2023; Du et al., 2022].Source: §5.2 · p. 8

5.3 Causal Intervention Solutions

  • Prior causal debiasing and shortcut-mitigation work includes invariant learning [Tian et al., 2022; Zhou et al., 2023a], CausaLM, and counterfactual methods.The passage notes limitations in targeting white-box models and requirements for retraining.Source: §5.3 · p. 8

6 Conclusion

The paper introduces SLANG to assess LLM comprehension of emerging linguistic trends and proposes FOCUS, which uses causal inference to enhance understanding of new concepts. Experiments report enhanced adaptation to evolving online language with FOCUS.

  • SLANG assesses LLM proficiency in comprehending emerging linguistic trends, including slang and memes.Source: §6 · p. 8
  • FOCUS uses causal inference to enhance understanding of new concepts and is reported to surpass other methods in precision and relevance.Source: §6 · p. 8
  • The benchmark uses UrbanDictionary-derived factual instances and counterfactual instances made by altering real-world examples to test adaptation to hypothetical scenarios.Source: §6 · p. 8

Limitations

The paper identifies language coverage, computational complexity, and downstream data access as limitations of its approach. These constraints include potential delays on mobile devices and time-consuming, variable data collection.

  • The study does not fully address linguistic evolution in non-English or morphologically rich languages.It calls for exploring a wider range of linguistic scenarios and extending the methodology to other languages and contexts.Source: Limitations · p. 9
  • FOCUS has higher computational complexity than some traditional approaches, which may increase demands and delay deployment on mobile devices.The text identifies real-time applications as potentially hindered and calls for optimization for mobile and other constrained environments.Source: Limitations · p. 9
  • Downstream use may require external tools to gather new-concept data when sources are not readily accessible, making collection time-consuming and task-variable.Source: Limitations · p. 9

Ethics Statement

The ethics statement cautions that improved understanding of Internet language does not eliminate harmful-content propagation. The experimental setup is presented as a way to support thorough, replicable evaluation.

Ethics Statement

  • SLANG and FOCUS cannot entirely eliminate harmful-content propagation, so users should exercise caution and cultural sensitivity when interpreting slang and memes.The statement emphasizes avoiding reinforcement of stereotypes or biases and respecting diverse linguistic origins.Source: Ethics Statement · p. 9

A Detail Experimental Setup

A.1 Models

The evaluation spans closed- and open-source models and prompting approaches, with filtered samples and standardized definitions supporting comparison. The section also reports evaluation costs and outlines additional results.

A.2 API Parameters

  • The authors state that API hyperparameters were held consistent for comparability, but the supplied passage does not list their values.Source: §A.2 · p. 13

A.3 Preprocessing

  • The test set contains 408 of 7,220 samples, selected through the paper’s filtering strategy.The authors compare this count with sample sizes in MaliciousInstruct, HumanEval, AdvBench, and HarmBench.Source: §A.3 · p. 13
  • The selected test samples are described as novel to the LLMs, with quality and diversity illustrated in Figure 6.Source: §A.3 · p. 13

A.4 Costs

  • Closed-source model experiments cost approximately $500, while open-source experiments used four NVIDIA Tesla A100 GPUs for 14 hours.The stated cost includes API discounts.Source: §A.4 · p. 13

B Explanation Standardization

  • User-generated definitions varied in style and format, so the authors standardized them using a template for meaning, usage context, and associated details.The template is: “[P] refers to [B]. It is often used [C]. This expression [A].”Source: B Explanation Standardization · p. 13

C Additional Experimental Results

C.1 Factual Dataset

FOCUS is reported to perform strongly on factual and counterfactual datasets, with component ablations showing performance drops. The section also describes novelty checks and validation of user-vote filtering.

C.4 Discussion

  • Across both datasets, FOCUS is reported to perform consistently, while GPT-3.5’s lower recall is attributed to its preference for shorter answers.The authors say this preference does not diminish FOCUS’s overall effectiveness.Source: §C.4 · p. 14

D Needle In A Haystack Test

C.1 Factual Dataset

  • On the factual dataset, FOCUS gives GPT-4 an F1 score of 0.4446 and Claude 3 Opus an F1 score of 0.4596, both reported as highest-performing results.The results are shown in Table 4.Source: §C.1 · pp. 13–14, Table 4 · p. 15

D.3 Discussion

  • For this tailored dataset subset, cutoff-date filtering was more stringent than the needle test; the authors say that test is essential for assessing novelty in the full dataset of over 180,000 entries.Source: §D.3 · p. 15

E Validation of User-generated Votes

C.2 Counterfactual Dataset

  • On the counterfactual dataset, FOCUS gives GPT-4 an F1 score of 0.4532, while Claude 3 Opus records an F1 score of 0.4636.The results are shown in Table 5.Source: §C.2 · p. 14, Table 5 · p. 16

C.3 Ablation Study

  • Removing the MEI and ERI components leads to a significant performance drop across the tested models on the factual dataset.The ablation results are shown in Table 6.Source: §C.3 · p. 14, Table 6 · p. 16

E.3 Results

The user-vote filtering method shows high recall and precision and very high consistency with volunteer judgments.

E.3 Results

  • 95%–99% recall, 91.67%–94.12% precision, and 96.50%–98.00% consistency are reported for the user-votes filtering method against human judgments.Source: §E.3 · p. 16

F Dataset Categorization

The paper describes how SLANG categorizes emerging slang and presents a flexible dataset pipeline, model-agnostic benchmark, and FOCUS approach for adapting to new language. It also defines the key terms used to describe concepts and linguistic change.

F Dataset Categorization

  • Five English-speaking volunteers independently categorized 408 new concepts into eight broad slang categories.The listed categories include abbreviations and acronyms, technical and internet terms, gaming and subculture jargon, sensitive-topic euphemisms, social media shortcuts, and generational slang.Source: F Dataset Categorization · p. 16
  • The volunteer-based categorization aimed to avoid overrepresenting any single slang category, and the dataset spans diverse expressions.Figure 6 shows the distribution of internet slang categories; Tables 7 and 8 address vote-filtering consistency and human-reviewed recall and precision.Source: F Dataset Categorization · p. 17, Figure 6 · p. 17, Table 7 · p. 17

G Summary of Contributions

  • SLANG is presented as a model-agnostic framework for standardized evaluation of LLM adaptability to evolving linguistic phenomena across datasets and architectures.The paper frames it as broader than the datasets and models used in its experiments.Source: G Summary of Contributions · pp. 17–18
  • An open-source toolbox supports continuous collection, cleaning, and processing of data from varied sources, including social media, forums, and digital publications.The pipeline is designed to be adaptable to different platforms, domains, and research questions.Source: G Summary of Contributions · pp. 17–18
  • FOCUS is presented as a model-agnostic way to adapt LLMs to emerging language on the fly without retraining or RAG.The paper describes its potential applicability across a broader range of models and architectures.Source: G Summary of Contributions · p. 18

H Definitions

  • A concept is a new idea or phenomenon emerging in language, an expression conveys it, deeper meaning goes beyond literal wording, and a linguistic shift incorporates concepts into language over time.Source: H Definitions · p. 18
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