Reformatted Alignment

• URL: http://arxiv.org/abs/2402.12219v2
• Date: Wed, 17 Apr 2024 15:03:19 GMT
• Title: Reformatted Alignment
• Authors: Run-Ze Fan, Xuefeng Li, Haoyang Zou, Junlong Li, Shwai He, Ethan Chern, Jiewen Hu, Pengfei Liu,
• Abstract summary: Current methods to improve data quality are either labor-intensive or prone to factual errors caused by hallucinations. This paper introduces a simple and effective approach named ReAlign, which reformats the responses of instruction data into a format that better aligns with pre-established criteria and the collated evidence. Experimentally, ReAlign significantly boosts the general alignment ability, math reasoning, factuality, and readability of the LLMs.
• Score: 27.79684742862816
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The quality of finetuning data is crucial for aligning large language models (LLMs) with human values. Current methods to improve data quality are either labor-intensive or prone to factual errors caused by LLM hallucinations. This paper explores elevating the quality of existing instruction data to better align with human values, introducing a simple and effective approach named ReAlign, which reformats the responses of instruction data into a format that better aligns with pre-established criteria and the collated evidence. This approach minimizes human annotation, hallucination, and the difficulty in scaling, remaining orthogonal to existing alignment techniques. Experimentally, ReAlign significantly boosts the general alignment ability, math reasoning, factuality, and readability of the LLMs. Encouragingly, without introducing any additional data or advanced training techniques, and merely by reformatting the response, LLaMA-2-13B's mathematical reasoning ability on GSM8K can be improved from 46.77% to 56.63% in accuracy. Additionally, a mere 5% of ReAlign data yields a 67% boost in general alignment ability measured by the Alpaca dataset. This work highlights the need for further research into the science and mechanistic interpretability of LLMs. We have made the associated code and data publicly accessible to support future studies at https://github.com/GAIR-NLP/ReAlign.

Related papers

• Reward-Augmented Data Enhances Direct Preference Alignment of LLMs [63.32585910975191]
  We introduce reward-conditioned Large Language Models (LLMs) that learn from the entire spectrum of response quality within the dataset. We propose an effective yet simple data relabeling method that conditions the preference pairs on quality scores to construct a reward-augmented dataset.
  arXiv  Detail & Related papers  (2024-10-10T16:01:51Z)
• FactAlign: Long-form Factuality Alignment of Large Language Models [35.067998820937284]
  Large language models have demonstrated significant potential as the next-generation information access engines. We propose FactAlign, a novel alignment framework designed to enhance the factuality of long-form responses. Our experiments on open-domain prompts and information-seeking questions demonstrate that FactAlign significantly improves the factual accuracy of LLM responses.
  arXiv  Detail & Related papers  (2024-10-02T16:03:13Z)
• Not Everything is All You Need: Toward Low-Redundant Optimization for Large Language Model Alignment [126.34547428473968]
  Large language models (LLMs) are still struggling in aligning with human preference in complex tasks and scenarios. We propose a low-redundant alignment method named textbfALLO, focusing on optimizing the most related neurons with the most useful supervised signals. Experimental results on 10 datasets have shown the effectiveness of ALLO.
  arXiv  Detail & Related papers  (2024-06-18T13:34:40Z)
• Aligning Large Language Models with Self-generated Preference Data [72.99676237703099]
  We propose a new framework that boosts the alignment of large language models (LLMs) with human preferences. Our key idea is leveraging the human prior knowledge within the small (seed) data. We introduce a noise-aware preference learning algorithm to mitigate the risk of low quality within generated preference data.
  arXiv  Detail & Related papers  (2024-06-06T18:01:02Z)
• Linear Alignment: A Closed-form Solution for Aligning Human Preferences without Tuning and Feedback [70.32795295142648]
  Linear alignment is a novel algorithm that aligns language models with human preferences in one single inference step. Experiments on both general and personalized preference datasets demonstrate that linear alignment significantly enhances the performance and efficiency of LLM alignment.
  arXiv  Detail & Related papers  (2024-01-21T10:46:23Z)
• Beyond Imitation: Leveraging Fine-grained Quality Signals for Alignment [105.34140537748546]
  We propose an improved alignment approach named FIGA. Different from prior methods, we incorporate fine-grained quality signals that are derived by contrasting good and bad responses. Our approach has made two major contributions. Firstly, we curate a refined alignment dataset that pairs initial responses and the corresponding revised ones. Secondly, we devise a new loss function can leverage fine-grained quality signals to instruct the learning of LLMs for alignment.
  arXiv  Detail & Related papers  (2023-11-07T15:36:40Z)
• RAIN: Your Language Models Can Align Themselves without Finetuning [25.703729145091483]
  Large language models (LLMs) often demonstrate inconsistencies with human preferences. We show that unaligned LLMs can directly produce responses consistent with human preferences via self-boosting. We introduce a novel inference method, Rewindable Auto-regressive INference (RAIN), that allows pre-trained LLMs to evaluate their own generation.
  arXiv  Detail & Related papers  (2023-09-13T17:59:09Z)
• AlpaGasus: Training A Better Alpaca with Fewer Data [93.6949102689243]
  We propose a simple and effective data selection strategy that automatically identifies and filters out low-quality data. We introduce AlpaGasus, which is finetuned on only 9k high-quality data filtered from the 52k Alpaca data. AlpaGasus significantly outperforms the original Alpaca on multiple test sets and the controlled human evaluation.
  arXiv  Detail & Related papers  (2023-07-17T17:59:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.