Challenge: specialised language models (LMs) have shown to exhibit lower perplexity and higher downstream performance across the board.
Approach: They propose a benchmark for NLP evaluation in social media, SuperTweetEval.
Outcome: The proposed benchmark shows that social media models perform better when compared to general-purpose models, metrics and benchmarks.

Similar Papers

TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification (2020.findings-emnlp)

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Challenge: Modern NLP systems are typically ill-equipped when applied to noisy user-generated text.
Approach: They propose a new evaluation framework consisting of seven Twitter-specific classification tasks.
Outcome: The proposed framework is based on seven heterogeneous Twitter-specific classification tasks.
TweetNLP: Cutting-Edge Natural Language Processing for Social Media (2022.emnlp-demos)

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Challenge: TweetNLP is an integrated platform for natural language processing in social media.
Approach: They propose a Python-based platform for natural language processing in social media that supports a variety of NLP tasks.
Outcome: The proposed platform supports generic focus areas such as sentiment analysis and named entity recognition, as well as social media-specific tasks such as emoji prediction and offensive language identification.
A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets (2023.findings-acl)

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Challenge: Currently, the evaluation of large language models (LLMs) such as ChatGPT in academic datasets is difficult due to the difficulty of evaluating the generative outputs produced by this model against the ground truth.
Approach: They evaluate ChatGPT across 140 tasks and analyze 255K responses it generates in academic datasets.
Outcome: The proposed model performs well on 140 tasks and generates 255K responses in these datasets.
GEMv2: Multilingual NLG Benchmarking in a Single Line of Code (2022.emnlp-demos)

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Challenge: Evaluations in machine learning rarely use the latest metrics, datasets, or human evaluation in favor of remaining compatible with prior work.
Approach: They propose to use the Generation, Evaluation, and Metrics Benchmark to integrate new evaluation methods into existing evaluations.
Outcome: The proposed evaluation infrastructure bridges the gap between the advantages of leaderboards and in-depth and evolving evaluations by allowing model developers to benefit from each other's work.
LEXTREME: A Multi-Lingual and Multi-Task Benchmark for the Legal Domain (2023.findings-emnlp)

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Challenge: Recent advances in legal NLP have led to a rapid growth of the field . however, many benchmarks are available only in English and no multilingual benchmark exists .
Approach: They propose to use 11 datasets covering 24 languages to compare NLP models.
Outcome: The proposed benchmarks show that even the best baseline only achieves modest results and ChatGPT struggles with many tasks.
Exploiting Twitter as Source of Large Corpora of Weakly Similar Pairs for Semantic Sentence Embeddings (2021.emnlp-main)

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Challenge: Semantic sentence embeddings are usually supervisedly built minimizing distances between pairs of embeddable sentences labelled as semantically similar by annotators.
Approach: They propose a language-independent approach to build large datasets of pairs of informal texts weakly similar, without manual human effort, exploiting Twitter’s powerful signals of relatedness: replies and quotes of tweets.
Outcome: The proposed model learns classical Semantic Textual Similarity, and excels on tasks where pairs of sentences are not exact paraphrases.
How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impact (2021.findings-acl)

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Challenge: Recent years have seen many breakthroughs in natural language processing (NLP), transitioning it from a mostly theoretical field to one with many real-world applications.
Approach: They propose a moral philosophy definition of social good and a framework to evaluate the direct and indirect real-world impact of NLP tasks.
Outcome: The proposed framework evaluates the direct and indirect real-world impact of NLP tasks and adopts the methodology of global priorities research to identify priority causes for NLP research.
Benchmarking Meta-embeddings: What Works and What Does Not (2021.findings-emnlp)

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Challenge: Existing methods to build meta-embeddings have been evaluated using a variety of methods and datasets, which makes it difficult to draw meaningful conclusions regarding the merits of each approach.
Approach: They propose a unified framework for a fair and objective meta-embedding evaluation using intrinsic and extrinsic tasks.
Outcome: The proposed framework outperforms existing methods on intrinsic and extrinsic evaluation benchmarks and outperformed existing methods.
USB: A Unified Summarization Benchmark Across Tasks and Domains (2023.findings-emnlp)

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Challenge: Existing summarization benchmarks lack the rich annotations needed to address important problems related to control and reliability.
Approach: They propose a Wikipedia-derived summarization benchmark with crowd-sourced annotations . they find that fine-tuned models outperform larger few-shot prompted language models .
Outcome: The proposed model outperforms many-shot prompted language models on multiple tasks . the proposed model is based on Wikipedia annotations and can be used in other domains .
Returning the N to NLP: Towards Contextually Personalized Classification Models (2020.acl-main)

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Challenge: a recent study shows that NLP models treat language as universal, but that it is based on sociolinguistic research.
Approach: They propose to incorporate user-dependent, contextual personal and social aspects into neural NLP models by means of socially contextual personalization.
Outcome: The proposed approach could be adapted to better personalize the language of users . it outlines a possible direction to incorporate these aspects into neural NLP models .

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