Challenge: ChatGPT has been criticized for its lack of accuracy and coherence . authors argue that language models could replace search engines and make college essays obsolete .
Approach: a team of 10 domain experts conducts an initial assessment of language models using 100 expert-written questions.
Outcome: The results show that language models are mixed in their accuracy.

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Leveraging Large Language Models for NLG Evaluation: Advances and Challenges (2024.emnlp-main)

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Challenge: introducing Large Language Models (LLMs) has opened new avenues for assessing generated content quality, e.g., coherence, creativity, and context relevance.
Approach: They propose a taxonomy for organizing existing LLM-based evaluation metrics and a structured framework to understand and compare them.
Outcome: The proposed taxonomy offers a framework to understand and compare LLM-based evaluation methods.
MEGA: Multilingual Evaluation of Generative AI (2023.emnlp-main)

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Challenge: Large Large Models (LLMs) have shown impressive performance on many natural language processing tasks such as language understanding, reasoning, and language generation.
Approach: They present a framework for evaluating generative LLMs in the multilingual setting and provide directions for future progress in the field.
Outcome: The proposed framework evaluates generative models on 16 NLP datasets across 70 typologically diverse languages and compares them to state-of-the-art non-autoregressive models.
A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendations (2024.emnlp-main)

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Challenge: Large Language Models (LLMs) have gained significant attention due to their capabilities in performing diverse tasks across domains.
Approach: They review the primary challenges and limitations causing inconsistencies in evaluations . early models could generate coherent text but limited to simple tasks .
Outcome: The proposed evaluations are reproducible, reliable, and robust.
ChatGPT Beyond English: Towards a Comprehensive Evaluation of Large Language Models in Multilingual Learning (2023.findings-emnlp)

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Challenge: Recent advances in natural language processing (NLP) have led to significant breakthroughs in the field.
Approach: They evaluate ChatGPT over multiple tasks with diverse languages and large datasets to provide more comprehensive information for multilingual NLP applications.
Outcome: The proposed model can process and generate texts for multiple languages due to its multilingual training data.
Large Language Models for Generative Recommendation: A Survey and Visionary Discussions (2024.lrec-main)

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Challenge: Large language models (LLMs) have revolutionized the field of natural language processing but are not fully able to leverage the generative power of LLM.
Approach: They examine the progress, methods, and future directions of large language models . they examine what generative recommendation is, why RS should advance to generative recommendations .
Outcome: The proposed approach can be simplified to generate recommendations from the entire pool of items.
How Reliable are Model Diagnostics? (2021.findings-acl)

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Challenge: Contemporary statistical models trade off interpretability and simplicity for powerful parameterizations and inductive biases, enabling impressive performance.
Approach: They examine three recent models and find they are not yet reliable . they also formulate recommendations for practitioners and researchers .
Outcome: The proposed models are not as reliable as previously assumed, the authors argue . their findings suggest that they are needed for improving models and training setups .
Is ChatGPT a Financial Expert? Evaluating Language Models on Financial Natural Language Processing (2023.findings-emnlp)

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Challenge: Large language models (LLMs) have revolutionized general natural language preprocessing tasks, but their performance in financial domains is not evaluated comprehensively.
Approach: They propose a framework to evaluate financial language models on financial tasks . they compare performance of auto-encoding language models and ChatGPT .
Outcome: The proposed framework compares the performance of auto-encoding language models and the LLM ChatGPT on financial tasks.
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.
Large Language Models are Not Yet Human-Level Evaluators for Abstractive Summarization (2023.findings-emnlp)

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Challenge: ChatGPT and GPT-4 are popular as evaluation metric for complex generative tasks . however, they are not ready as human replacements due to significant limitations .
Approach: They conduct extensive analysis to examine the stability and reliability of LLMs as automatic evaluators for abstractive summarization.
Outcome: The proposed methods outperform the commonly used automatic metrics but are not ready for human evaluation due to significant limitations.
How Can We Know When Language Models Know? On the Calibration of Language Models for Question Answering (2021.tacl-1)

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Challenge: Recent studies have shown that language models capture different types of knowledge regarding facts or commonsense knowledge.
Approach: They examine how language models can be calibrated to make their confidence scores correlate better with the likelihood of correctness.
Outcome: The proposed calibration methods improve confidence scores on QA tasks and improve accuracy.

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