Papers Explained 325: Selective Self-to-Supervised Fine-Tuning (S3FT)
Selective Self-to-Supervised Fine-Tuning (S3FT) is a fine- tuning approach that first identifies the correct model responses from the training set by deploying an appropriate judge. Then, it fine-tunes the model using the correct model responses and the gold response (or its paraphrase) for the remaining samples. S3FT achieves better performance than the standard supervised fine-tuning (SFT) while improving generalization. By utilizing the model’s correct responses, S3FT reduces model specialization during the fine-tuning stage.
Selective Self-to-Supervised Fine-Tuning (S3FT)
S3FT (Self-Supervised Self-Training Fine-Tuning) aims to fine-tune large language models (LLMs) for specific tasks while minimizing the degradation of their general capabilities, a common issue with standard Supervised Fine-Tuning (SFT). It leverages two key observations:
- Multiple Valid Responses: Many NLP tasks can have multiple correct answers for the same input.
- Self-Learning Preservation: Teaching the model using its own generated language helps preserve its original distribution and mitigate catastrophic forgetting (losing previously learned knowledge).
Initial Prediction:
Given a training example (input xi, gold answer yi), the base model Mθ0 (pre-trained and instruction-tuned) generates a prediction ˆyi = Mθ0(xi).
Equivalence Check:
The method checks if ˆyi is equivalent to the gold answer yi. This can be done using:
- Heuristics: Simple checks like comparing key information or overall agreement.
- LLM Judge: A more powerful LLM assesses the semantic equivalence between ˆyi and yi.
Training Data Selection:
- If ˆyi is equivalent to yi: The pair (xi, ˆyi) is used for training. This reinforces the model’s existing knowledge and helps preserve its original distribution.
- If ˆyi is NOT equivalent to yi: The base model Mθ0 rephrases the gold answer yi in its own language, generating ˜yi = Mθ0([xi; yi]). This attempts to bridge the gap between the gold answer and the model’s own language style.
Second Equivalence Check:
If rephrasing was necessary, the method checks if ˜yi is equivalent to yi.
Final Training Data Selection:
- If ˜yi is equivalent to yi: The pair (xi, ˜yi) is used for training. This teaches the model the desired output while still using its own “language” and minimizing drift from its original distribution.
- If ˜yi is NOT equivalent to yi: The original pair (xi, yi) is used for training. This is a fallback to standard SFT when the model cannot generate a suitable paraphrase.
Mistral-instruct-v2 (7B) is used as the base model and the judge model. For all fine-tuning, Low-Rank Adaptation (LoRA) with a rank of 8, a scaling factor of 16 and a dropout of 0.1 is used.
Evaluation
- Improved In-Domain Performance: S3FT significantly outperforms the base model and SFT on in-domain datasets (GSM8K, MBPP, NQ). It achieves comparable performance to SDFT on NQ (reading comprehension).
- Mitigated Catastrophic Forgetting: S3FT demonstrates better generalization capabilities compared to SFT, showing significantly less performance degradation on out-of-domain benchmarks after fine-tuning. SFT suffers from substantial performance drops on these benchmarks, indicating catastrophic forgetting.
- Impact of Gold Response Paraphrasing: Using the model’s own correct responses as training targets (as in S3FT) leads to better performance and generalization. This is attributed to the fact that model-generated responses are often closer to the model’s own distribution than gold responses or even paraphrased gold responses. Training on gold responses can shift the model’s distribution, negatively impacting generalization.
Paper
Selective Self-to-Supervised Fine-Tuning for Generalization in Large Language Models 2502.08130
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