| Challenge: | a recent study examined the attribution and factuality of language models in domains . experts from various fields are using large language models for information-seeking scenarios . |
| Approach: | They evaluate language models' attribution and factuality by bringing domain experts in the loop . they collect expert-curated questions from 484 participants across 32 fields of study . |
| Outcome: | The results show that language models can provide factually correct answers in high-stakes fields, but they can also be harmful to experts. |
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| 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 . |
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Nan Hu, Jiaoyan Chen, Yike Wu, Guilin Qi, Hongru Wang, Sheng Bi, Yongrui Chen, Tongtong Wu, Jeff Z. Pan
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| Challenge: | Generative large language models (LLMs) incorporate external references to generate and support claims. however, evaluating the attribution remains an open problem. |
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| Challenge: | Recent proposed long-form question answering systems have shown promising capabilities, but attributing and verifying their generated abstractive answers can be difficult. |
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| Challenge: | Existing QA benchmarks that provide fixed answers to debatable questions are inadequate for evaluating their performance. |
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| Challenge: | Large Language Models (LLMs) have revolutionized natural language processing, but their success remains limited to high-resource domains. |
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ASQA: Factoid Questions Meet Long-Form Answers (2022.emnlp-main)
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| Challenge: | Recent progress on factoid question answering (QA) does not easily transfer to the task of long-form QA where the goal is to generate detailed explanations. |
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