Improving LLMs: Steering Towards Truth and Self-Evaluation
KordingLab · x · 2026-08-26
This post proposes two strategies for enhancing LLM output quality. First, in domains where 'truth' is definable (like math or coding), actively push the model towards that truth during training. Second, in open domains without clear truth definitions, use LLMs to perform 'self-evaluation' to check their own answers. These methods aim to move beyond simple probabilistic generation.
More from Research
- Goodfire's Nick Cammarata: neural networks are "already clean" in a possible interpretability breakthrough — tszzl · 2026-10-15
- tszzl: interpretability breakthrough feels like my understanding of neural nets jumped five years — tszzl · 2026-10-15
- LightSuite: fast cell detection and whole-brain atlas registration on a standard workstation — hisspikeness · 2026-10-15
- Pathway CSO pitches BDH as post-Transformer architecture uniting memory, reasoning, adaptation — schwentker · 2026-10-15
- AI-designed molecular scissors cut Alzheimer's proteins — but is it really AI? — gerardsans · 2026-10-15
- EmbodiedEvo tops RoboDojo at 38.95% success by letting robots inherit failed practice — thetripathi58 · 2026-10-15