A number of tasks requiring the creation or verification of factual assertions—corresponding to query answering, fact-checking, and even the generation of unconditional text—are relatively successfully handled by current language models (LMs). Nonetheless, growing evidence shows that LMs grow to be more vulnerable to producing erroneous but often repeated comments as size increases. They’re removed from being completely dependable. The undeniable fact that LMs have several affordances for resolving factual generation tasks further complicates issues. 

They may be used each generatively (by asking for the almost certainly answer to an issue) and discriminatively (by presenting a (question-answer pair and asking whether the reply is appropriate), but these two methods sometimes yield different results. Generative methods can fail when probability mass is spread across multiple contradictory answers, whereas discriminative methods can fail due to miscalibration or a subtle dependence on the query. How should they extract an LM’s best estimate concerning the truth from these chaotic and steadily contradicting signals? The CONSENSUS GAME, a signaling game, is utilized in this research by researchers from MIT to supply a way for bridging generative and discriminative LM decoding processes. 

A DISCRIMINATOR agent must convey an abstract correct or mistaken value to a GENERATOR agent at a high level. Still, it may well only achieve this by utilizing a limited variety of potential natural language strings. It seems to reason that a combined policy, where the GENERATOR and DISCRIMINATOR agree on the task of strings to correctness values, can be a successful approach for this game. They’ll examine an approach like that to search out candidates everyone agrees are right. A multi-step game with a difficult (string-valued) motion space should be solved to do that. No-regret learning algorithms have been popular recently because the go-to method for calculating winning tactics in games like Poker, Stratego, and Diplomacy. 

Here, they display that they might also be used for tasks involving the creation of free-form languages. This game-theoretic approach to LM decoding is often known as EQUILIBRIUM-RANKING. When utilized in 6 benchmarks for question-answering performance (MMLU, ARC, RACE, HHH, TruthfulQA, and GSM8K), EQUILIBRIUM-RANKING significantly outperforms the generative, discriminative, and mixed decoding techniques now in use. In a broader sense, their findings display how the game-theoretic toolset could also be used to formalize and enhance coherence in LMs. The accuracy of factual tasks also improves in consequence of increased coherence.

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