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New paper from Salesforce AI Research. The authors found that joint training, continual pre-training (CPT), and instruction tuning with a 50/50 data split achieve better results than sequential training. Their 8B parameter model outperformed larger 70B models on financial tasks.
Down-sampling CPT data to match IT data size improved performance on CFA Challenge exams from 34.44% to 55.56%, while maintaining strong general knowledge capabilities as shown by comparable or better performance on general knowledge benchmarks like AI2-ARC and MMLU.
Technical implementation involved two-stage training: Group 1 utilized 3.84B tokens from web and basic texts, followed by Group 2, which used 1.66B tokens from domain-specific books. Their preference alignment method used generative reward models to identify and correct reasoning errors rather than just rating full solutions.
Evaluation on 91,872 samples across 31 tasks showed their Llama-Fin model achieving 91.13% accuracy on sentiment analysis (FPB) and 95.32% on FiQA SA, exceeding GPT-4's performance of 82.16% and 68.51%, respectively, on these benchmarks.
It could be useful for many financial companies looking to build AI pipelines.
Interesting read, but neither the model nor GitHub repo is accessible yet. The key insight for AI builders is that with small models - it is fully possible to outperform much bigger models.
https://arxiv.org/abs/2501.04961
Down-sampling CPT data to match IT data size improved performance on CFA Challenge exams from 34.44% to 55.56%, while maintaining strong general knowledge capabilities as shown by comparable or better performance on general knowledge benchmarks like AI2-ARC and MMLU.
Technical implementation involved two-stage training: Group 1 utilized 3.84B tokens from web and basic texts, followed by Group 2, which used 1.66B tokens from domain-specific books. Their preference alignment method used generative reward models to identify and correct reasoning errors rather than just rating full solutions.
Evaluation on 91,872 samples across 31 tasks showed their Llama-Fin model achieving 91.13% accuracy on sentiment analysis (FPB) and 95.32% on FiQA SA, exceeding GPT-4's performance of 82.16% and 68.51%, respectively, on these benchmarks.
It could be useful for many financial companies looking to build AI pipelines.
Interesting read, but neither the model nor GitHub repo is accessible yet. The key insight for AI builders is that with small models - it is fully possible to outperform much bigger models.
https://arxiv.org/abs/2501.04961
