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Hierarchical Text Generation and Planning for Strategic Dialogue

ICML 2018 · Large language models › Language, safety and interpretability · approach: Faithfulness of LLM explanations · 48 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Pretrain discrete sentence representations to predict dialogue continuation and actionsNegotiation dialogue generation · Negotiation testperplexity: 5.46 -> 5.24Helped
Pretrain discrete sentence representations to predict dialogue continuation and actionsNegotiation self-play · Self-play negotiationsscore vs. RNN: 4.68 -> 6.75Helped
Add hierarchical sentence-level recurrence over messagesNegotiation dialogue generation · Negotiation testperplexity: 5.47 -> 5.21Helped
Add rollout planning (ablation)Full model · Self-play negotiationsscore vs. no rollouts: 5.08 -> 7.81Helped
Use distinct latent intents for rollout candidates (ablation)Full model with rollouts · Self-play negotiationsscore vs. baseline rollouts: 6.57 -> 7.36Helped
Fine-tune only the intent predictor with reinforcement learning while keeping language parameters fixed (ablation)Full model · Self-play negotiationsreward at human-like language qualityHelped
Apply reinforcement learning to all model parameters (ablation)Full model · Self-play negotiationslanguage qualityHurt
Add rollout planning (ablation)Full model · Negotiations against humansscore vs. human: 5.42 -> 7.45Helped
Add rollout planning (ablation)RNN · Negotiations against humansscore vs. human: 5.30 -> 6.99Helped
Fine-tune with reinforcement learning (ablation)Full model · Negotiations against humansscore vs. human: 5.42 -> 6.26Helped
Fine-tune with reinforcement learning (ablation)RNN · Negotiations against humansscore vs. human: 5.30 -> 6.01Helped
Use discrete latent representations rather than the direct hierarchical continuous connection (ablation)Negotiation dialogue generation · Negotiation testperplexity: 5.21 -> 5.24No clear effect

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