Researchers find that retraining only small parts of AI models can cut costs and prevent forgetting
Enterprises often find that when they fine-tune models, one effective approach to making a large language model (LLM) fit for purpose and grounded in data is to have the model lose some of its abilities. After fine-tuning, some models “forget” how to perform certain tasks or other tasks they already learned. Research from the University of Illinois Urbana-Champaign proposes a new method for retraining models that avoids “catastrophic forgetting,” in which the model loses some of its prior knowledge. The paper focuses on two specific LLMs that generate responses from images: LLaVA and Qwen 2.5-VL.The approach encourages enterprises to retrain only narrow parts of an LLM to avoid retraining the entire model and incurring a significant increase in compute costs. The team claims that [...]
Rating
Innovation
Pricing
Technology
Usability
We have discovered similar tools to what you are looking for. Check out our suggestions for similar AI tools.
Fine-tuning forgets. RAG leaks context. Hypernetworks build the model your agent needs on demand.
Enterprise teams keep watching the same thing happen. An AI agent demos beautifully, goes to production, and stalls: it runs for a short stretch, then needs a human to top up its context and check its [...]
MIT's MeMo lets teams swap in a better LLM without retraining — and performance jumps 26%
Enabling LLMs to acquire new knowledge after training remains a major hurdle for enterprise AI — current solutions are either too expensive, too slow, or constrained by context window limits.MeMo, a [...]
MeMo's memory model lets teams upgrade their LLM without retraining it — and performance jumps 26%
Enabling LLMs to acquire new knowledge after training remains a major hurdle for enterprise AI — current solutions are either too expensive, too slow, or constrained by context window limits.MeMo, a [...]
MIT's new fine-tuning method lets LLMs learn new skills without losing old ones
When enterprises fine-tune LLMs for new tasks, they risk breaking everything the models already know. This forces companies to maintain separate models for every skill.Researchers at MIT, the Improbab [...]
98% of market researchers use AI daily, but 4 in 10 say it makes errors — revealing a major trust problem
Market researchers have embraced artificial intelligence at a staggering pace, with 98% of professionals now incorporating AI tools into their work and 72% using them daily or more frequently, accordi [...]
Attention ISN'T all you need?! New Qwen3 variant Brumby-14B-Base leverages Power Retention technique
When the transformer architecture was introduced in 2017 in the now seminal Google paper "Attention Is All You Need," it became an instant cornerstone of modern artificial intelligence. Ever [...]
Self-improving language models are becoming reality with MIT's updated SEAL technique
Researchers at the Massachusetts Institute of Technology (MIT) are gaining renewed attention for developing and open sourcing a technique that allows large language models (LLMs) — like those underp [...]