Fine-tuning language models to find agreement among humans with diverse preferences.
Michiel A. BakkerMartin J. ChadwickHannah SheahanMichael Henry TesslerLucy Campbell-GillinghamJan BalaguerNat McAleeseAmelia GlaeseJohn AslanidesMatt M. BotvinickChristopher SummerfieldPublished in: NeurIPS (2022)
Keyphrases
- language model
- fine tuning
- language modeling
- document retrieval
- n gram
- viable alternative
- language modelling
- information retrieval
- fine tune
- probabilistic model
- speech recognition
- user preferences
- query expansion
- test collection
- document ranking
- retrieval model
- ad hoc information retrieval
- statistical language models
- smoothing methods
- query terms
- fine tuned
- context sensitive
- passage retrieval
- document length
- pseudo relevance feedback
- language models for information retrieval
- term dependencies
- translation model
- relevance model
- word error rate
- vector space model
- language model for information retrieval