mlsanityData to 7 Oct 2026

Alignment For Performance Improvement in Conversation Bots

arXiv 2406.18954 ↗First posted 27 Jun 2024cs.LG cs.AIcited by 0

On the map: LLM alignment / RLHF / preference optimisation → LLM preference optimization methods

also in 1 more
Abstract (arXiv)

This paper shows that alignment methods can achieve superior adherence to guardrails compared to instruction fine-tuning alone in conversational agents, also known as bots, within predefined guidelines or 'guardrails'. It examines traditional training approaches such as instruction fine-tuning and the recent advancements in direct alignment methods like Identity Preference Optimization (IPO), and Kahneman-Tversky Optimization (KTO). The effectiveness of alignment techniques both pre and post-instruction tuning is highlighted, illustrating their potential to optimize conversational bots in domains that require strict adherence to specified rules, such as customer care.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in LLM preference optimization methods, one of 10 directions in LLM alignment / RLHF / preference optimisation. Builds on 11 of its 13 references that are in the map. First posted on arXiv 27 Jun 2024.

Builds on

11 of 13 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023A General Theoretical Paradigm to Understand Learning from Human Preferences18 Oct
  2. 2022Training language models to follow instructions with human feedback4 Mar

Other references · 9

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  3. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 7,916
  4. 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,634
  5. 2023Mistral 7Bcited by 3,065
  6. 2023An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuningcited by 622
  7. 2023SLiC-HF: Sequence Likelihood Calibration with Human Feedbackcited by 395
  8. 2021Understanding Dataset Difficulty with V-Usable Informationcited by 288
  9. 2022Is Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimizationcited by 244

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2023Align on the Fly: Adapting Chatbot Behavior to Established Norms
  2. 2024LAB: Large-Scale Alignment for ChatBots
  3. 2025Conversational Alignment with Artificial Intelligence in Context
  4. 2023CycleAlign: Iterative Distillation from Black-box LLM to White-box Models for Better Human Alignment
  5. 2023Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision
  6. 2026HAL: Inducing Human-likeness in LLMs with Alignment
  7. 2021A General Language Assistant as a Laboratory for Alignment
  8. 2023OpenAssistant Conversations -- Democratizing Large Language Model Alignment