Analyzing the Impact of Neologisms on Language Models

Conceitos Básicos

Neologisms impact language models significantly, affecting translation quality and downstream tasks.

Resumo

The study focuses on the impact of neologisms on Large Language Models (LLMs). It explores temporal drift in LLMs due to emerging neologisms, leading to model degradation. The research introduces NEO-BENCH, a benchmark to evaluate LLMs' ability to handle neologisms across various tasks. Results show that translating neologisms poses a challenge for models, with significant differences in performance based on linguistic origins of words. Older LLMs perform worse compared to newer models, highlighting the importance of adapting to evolving language changes. The study also analyzes perplexity rankings and downstream task performance for different types of neologisms - lexical, morphological, and semantic.

Estatísticas

Model performance decreases by 43% when translating sentences containing neologisms.
Neologism perplexities are significantly higher than pre-existing words.
Semantic neologisms produce the lowest perplexities but worst performance in generation tasks.

Citações

"A single neologism can dramatically affect model output." - Study findings

Principais Insights Extraídos De

NEO-BENCH

by Jonathan Zhe... às arxiv.org 03-19-2024

https://arxiv.org/pdf/2402.12261.pdf

Perguntas Mais Profundas

How do newer language models handle neologisms compared to older models?

In the context provided, it is observed that newer language models tend to handle neologisms better than older models. The study shows that newer models, such as GPT-3.5 and GPT-4, perform significantly better in tasks involving neologisms compared to older models like BART and T5. One key factor contributing to this difference is that newer models are trained on more recent data containing a wider range of neologisms emerging around 2020-2023. These newer models have algorithmic improvements and are exposed to a larger volume of diverse linguistic content.
The results indicate that there is an average improvement of 54.7% in downstream task performance between the newest and oldest language models evaluated in handling neologisms. This suggests that the ability of LLMs to adapt to new words and linguistic changes improves with advancements in model architecture, training data diversity, and algorithmic enhancements over time.

How can the benchmark created in this study be applied to other languages or dialects?

The benchmark developed in this study for evaluating Large Language Models (LLMs) on their ability to understand and process neologisms can be adapted for use with other languages or dialects by following a similar methodology but tailored specifically for each target language or dialect.

Data Collection:

Collecting a diverse set of recent neologisms specific to the target language or dialect using multiple methods such as news articles, social media platforms, dictionaries, etc.

Benchmark Tasks:

Designing tasks like Machine Translation evaluation with human judgment metrics, Cloze Question Answering tests using context-based questions with neologism answers, Definition Generation assessments for generating accurate definitions of given neologisms.

Model Evaluation:

Evaluating different LLMs on their performance with respect to understanding and processing these newly collected terms through perplexity rankings, accuracy scores across various tasks.

Ethical Considerations:

Ensuring ethical considerations when collecting data on potentially sensitive information within different cultural contexts or regions where certain terms may carry offensive meanings.

By customizing the benchmark creation process according to the linguistic nuances and characteristics of each specific language or dialect under consideration, researchers can effectively evaluate how well LLMs generalize towards handling new word forms unique to those linguistic variations.

What ethical considerations should be taken into account when collecting and analyzing data on neologisms?

When collecting and analyzing data related to neologisms ethically significant considerations include:

Privacy Concerns: Ensure personal identifiable information (PII) is not included inadvertently while gathering datasets from sources like social media platforms.

Offensive Content: Be cautious about including offensive terms during dataset collection; ensure they are handled sensitively without perpetuating stereotypes or biases.

Cultural Sensitivity: Respect cultural differences by avoiding derogatory stereotypes associated with certain demographic groups while selecting examples for analysis.

Consent & Anonymity: Obtain consent if utilizing user-generated content; maintain anonymity when referencing individuals' contributions unless explicitly permitted otherwise.

5 .Bias Mitigation: Strive towards unbiased representation by considering potential biases present within collected datasets; aim for inclusivity across all demographics represented within the dataset
6 .Transparency & Accountability: Maintain transparency regarding data collection methods used; adhere strictlyto guidelines ensuring accountability throughoutthe research process
By adhering closelyto these ethical principles,data analystscan conduct thoroughand responsible analysesofneology-relateddatawhile upholdingintegrityandrespectforindividualsand communitiesinvolvedinthe researchprocesses