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Raman

TheDetective
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Natural Language Processing

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authored a paper 2 days ago
Do Audio-Language Models Understand Linguistic Variations?
reacted to vladbogo's post with 👍 about 1 year ago
"A Closer Look at the Limitations of Instruction Tuning" is a new paper that explores the efficacy and limitations of Instruction Tuning (IT) in Large Language Models (LLMs) for conversational agents. The authors conduct a series of experiments using both LoRA fine-tuning (LFT) and standard full-parameter fine-tuning (SFT) across various LLMs and IT datasets. The key findings are: * LoRA fine-tuning (LFT) preserves the pre-training token distribution while SFT doesn't. This indicates that using LFT, post fine-tuning the model still heavily relies on the pre-training and doesn't acquire new information. * Dataset scaling is ineffective for LFT - experiments show that scaling the dataset size 52x or even 326x doesn't improve the performance. * LoRA fine-tuning mainly enhances response initiation and style without substantial knowledge enhancement. * Full-parameter fine-tuning tends to degrade LLM knowledge base and increase hallucination occurrences. * Popular other methods and adjustments fail to significantly outperform simple LoRA fine-tuned models in terms of conversational quality and accuracy. Congrats to the authors @Sreyan88 and others for their work! Paper: https://huggingface.co/papers/2402.05119
updated a model over 1 year ago
TheDetective/cross_ner_ai
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