Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques
Paper
•
2506.08060
•
Published
•
6
•
2
Asankhaya Sharma PRO
codelion
AI & ML interests
Creator of OptiLLM, OpenEvolve, Adaptive Classifier, and PTS. Pioneering a new category in AI infrastructure: inference-time compute for LLMs.
Recent Activity
liked
a model
6 minutes ago
google/gemma-3-1b-it
reacted
to
their
post
with ❤️
3 days ago
New Research: Theoretical Foundations for In-Context Learning in Transformers
I'm excited to share our latest theoretical work that formally proves an interesting property of large language models: base transformer models can approximate fine-tuned capabilities using only inference-time techniques like in-context learning.
The core question we investigated: Can specialized behaviors typically acquired through expensive supervised fine-tuning be elicited from base models without any parameter updates?
Our theoretical contribution: We provide a formal proof, grounded in the Turing completeness of transformers, showing that this is indeed possible under certain assumptions. The work establishes mathematical bounds on the minimal dataset sizes needed for approximation.
Key theoretical results:
- For text generation tasks: O(mV/ε²) examples suffice (where m = number of contexts, V = vocabulary size, ε = error tolerance)
- For linear classification: O(d/ε) examples (where d = input dimension)
- Extensions to finite context scenarios with practical bounds
This work helps explain why techniques like few-shot prompting, retrieval-augmented generation, and in-context learning work so effectively in practice. It bridges formal computer science theory with empirical observations about modern language models.
While the assumptions are idealized (unbounded computational resources, full dataset access), the results provide mathematical foundations for understanding inference-time adaptation strategies that are increasingly important in AI deployment.
Paper: https://huggingface.co/papers/2506.08060
reacted
to
their
post
with ➕
3 days ago
New Research: Theoretical Foundations for In-Context Learning in Transformers
I'm excited to share our latest theoretical work that formally proves an interesting property of large language models: base transformer models can approximate fine-tuned capabilities using only inference-time techniques like in-context learning.
The core question we investigated: Can specialized behaviors typically acquired through expensive supervised fine-tuning be elicited from base models without any parameter updates?
Our theoretical contribution: We provide a formal proof, grounded in the Turing completeness of transformers, showing that this is indeed possible under certain assumptions. The work establishes mathematical bounds on the minimal dataset sizes needed for approximation.
Key theoretical results:
- For text generation tasks: O(mV/ε²) examples suffice (where m = number of contexts, V = vocabulary size, ε = error tolerance)
- For linear classification: O(d/ε) examples (where d = input dimension)
- Extensions to finite context scenarios with practical bounds
This work helps explain why techniques like few-shot prompting, retrieval-augmented generation, and in-context learning work so effectively in practice. It bridges formal computer science theory with empirical observations about modern language models.
While the assumptions are idealized (unbounded computational resources, full dataset access), the results provide mathematical foundations for understanding inference-time adaptation strategies that are increasingly important in AI deployment.
Paper: https://huggingface.co/papers/2506.08060
Organizations
codelion's activity
commented
a
paper
3 days ago
commented
a
paper
11 days ago
commented
a
paper
12 days ago
commented
a
paper
about 1 month ago
Upload steering_vectors.jsonl
#1 opened about 1 month ago
by
codelion
Adding `safetensors` variant of this model
#1 opened about 1 month ago
by
SFconvertbot
commented
a
paper
about 1 month ago
Add link to paper
1
#3 opened 2 months ago
by
nielsr
Implement other approach that optillm supported?
2
#5 opened 3 months ago
by
melekuk
Update app.py
#1 opened 4 months ago
by
codelion
commented
a
paper
4 months ago
Issue with Flash attention while running Janus-Pro-1B model locally on Mac (Solved)
1
#8 opened 4 months ago
by
saurabhksa1
commented
2
papers
4 months ago
What do the labels mean?
1
#21 opened 10 months ago
by
gleni2101
commented
a
paper
5 months ago
Nice! Any chance we can have access to the unquantified model files?
2
#1 opened 5 months ago
by
Joseph717171
Can't reproduce the evaluation result of GPQA dataset
➕
1
5
#47 opened 6 months ago
by
Rinn000
commented
2
papers
5 months ago