Researchers have explored interventions on a language model trained to play chess, dubbed Chess-GPT. By manipulating the model's internal representations of the board state and player skill, they demonstrated a causal link between these representations and the model's output. This work addresses skepticism about whether large language models possess genuine world models or merely learn superficial patterns, showing that targeted edits can influence the model's playing strength and move generation. AI
IMPACT Investigates the depth of understanding in LLMs, potentially influencing how we evaluate and develop future models.
RANK_REASON Blog post detailing research on manipulating a language model's internal representations, with a paper accepted to a conference. [lever_c_demoted from research: ic=1 ai=1.0]
Read on HN — machine learning stories →
- Adam Karvonen
- Chess-GPT
- Conference on Language Modeling
- Douglas Hofstadter
- Elo
- Gary Marcus
- GPT-3
- Kevin Lacker
- Lichess
- Othello GPT
- PGN
- Stockfish
- Ernest Davis
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