SOCIALITE-LLAMA: An Instruction-Tuned Model for Social Scientific Tasks (2024.eacl-short)
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Gourab Dey, Adithya V Ganesan, Yash Kumar Lal, Manal Shah, Shreyashee Sinha, Matthew Matero, Salvatore Giorgi, Vivek Kulkarni, H. Schwartz
| Challenge: | Social science NLP tasks require large data to capture semantics and implicit pragmatics. |
| Approach: | They propose an open-source instruction tuning tool for social science NLP tasks that captures implicit pragmatic cues from text. |
| Outcome: | The proposed model matches or improves on a state-of-the-art, multi-task finetuned model on 80% of social tasks. |
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| Challenge: | Foundational large language models (LLMs) can be instruction-tuned to perform open-domain question answering, facilitating applications like chat assistants. |
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Israel Azime, Atnafu Tonja, Tadesse Belay, Mitiku Yohannes Fuge, Aman Wassie, Eyasu Jada, Yonas Chanie, Walelign Sewunetie, Seid Yimam
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| Challenge: | Existing instruction tuned large language models (LLMs) struggle to understand cross-lingual sociopragmatic meaning (SM) lack of comprehensive investigation into their ability to understand SM is partly due to SM not being adequately represented in any of the existing benchmarks. |
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| Challenge: | Pre-trained large language models retain task-specific knowledge, but where and to what extent they retain it remains unexplored. |
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