🔍 Today's pick in Interpretability & Analysis of LMs: ReFT: Representation Finetuning for Language Models by 
text
🔍 Today's pick in Interpretability & Analysis of LMs: ReFT: Representation Finetuning for Language Models by 
@zhengxuanzenwu
mention
zhengxuanzenwu
text
@aryaman
mention
aryaman
 Z. Wang 
text
 Z. Wang 
@atticusg
mention
atticusg
 D. Jurafsky 
text
 D. Jurafsky 
@manning
mention
manning
text
@cgpotts
mention
cgpotts
new_line
new_line
This work introduces Representation fine-tuning (ReFT), a framework using learned inference-time interventions as efficient yet effective alternatives to PEFT weight adaptation. LoReFT, a ReFT variant intervening linearly on a representation subspaces, is evaluated against several PEFT approaches showing SOTA performances across popular benchmark with 10-50x speedup. The 🤗-compatible pyreft library is introduced to simplify ReFT usage.
text
This work introduces Representation fine-tuning (ReFT), a framework using learned inference-time interventions as efficient yet effective alternatives to PEFT weight adaptation. LoReFT, a ReFT variant intervening linearly on a representation subspaces, is evaluated against several PEFT approaches showing SOTA performances across popular benchmark with 10-50x speedup. The 🤗-compatible pyreft library is introduced to simplify ReFT usage.
new_line
new_line
This is one of the most convincing practical applications of interpretability methods/insights I've seen in recent years, and I'm looking forward to people combining this with methods to disentangle features like SAEs and Backpack LMs for making interventions more interpretable!
text
This is one of the most convincing practical applications of interpretability methods/insights I've seen in recent years, and I'm looking forward to people combining this with methods to disentangle features like SAEs and Backpack LMs for making interventions more interpretable!
new_line
new_line
📄 Paper: 
text
📄 Paper: 
ReFT: Representation Finetuning for Language Models (2404.03592)
https://huggingface.co/papers/2404.03592
2404.03592
paper
resource
https://huggingface.co/papers/2404.03592
new_line
new_line
🔍 All daily picks: 
text
🔍 All daily picks: 
https://huggingface.co/collections/gsarti/daily-picks-in-interpretability-and-analysis-of-lms-65ae3339949c5675d25de2f9
https://huggingface.co/collections/gsarti/daily-picks-in-interpretability-and-analysis-of-lms-65ae3339949c5675d25de2f9
link
🔍 Today's pick in Interpretability & Analysis of LMs: ReFT: Representation Finetuning for Language Models by @zhengxuanzenwu @aryaman Z. Wang @atticusg D. Jurafsky @manning @cgpotts

This work introduces Representation fine-tuning (ReFT), a framework using learned inference-time interventions as efficient yet effective alternatives to PEFT weight adaptation. LoReFT, a ReFT variant intervening linearly on a representation subspaces, is evaluated against several PEFT approaches showing SOTA performances across popular benchmark with 10-50x speedup. The 🤗-compatible pyreft library is introduced to simplify ReFT usage.

This is one of the most convincing practical applications of interpretability methods/insights I've seen in recent years, and I'm looking forward to people combining this with methods to disentangle features like SAEs and Backpack LMs for making interventions more interpretable!

📄 Paper: https://huggingface.co/papers/2404.03592

🔍 All daily picks: https://huggingface.co/collections/gsarti/daily-picks-in-interpretability-and-analysis-of-lms-65ae3339949c5675d25de2f9
❤️
samusenps aryaman Jigya zhengxuanzenwu diwank srisree