AI Briefing[ACL 2024] Latest Trends and Key Insights in LLM Research - LG AI Research Blog
Key point
It introduces the latest research trends for overcoming the limitations of LLMs presented at ACL 2024 and LG AI Research's achievements.
Details
With the advancement of generative AI, LLMs are solving a variety of problems, but at the same time they are also revealing limitations such as Hallucination, ethical issues, and malicious attacks. Accordingly, research into methods for learning better data and sophisticated methodologies for evaluating LLM performance is being actively conducted.
In particular, the way text generation quality is evaluated has evolved from the existing statistics-based N-gram methods (BLEU, ROUGE) to embedding-based methods such as BERTScore, which capture contextual meaning. Recently, the RLHF technique, which trains a reward model using human feedback, has been drawing attention.
LG AI Research presented the following innovative research at ACL 2024.
- Multi-Objective Reward Modeling: Research on a reward model that adds regression and gating layers to evaluate responses from various perspectives and precisely reflect human preferences.
- Prometheus 2: Research on an open source language model designed to professionally evaluate other language models.
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