Large Language Models (LLMs) are rapidly surpassing human knowledge in many domains. While improving these models traditionally relies on costly human data, recent self-rewarding mechanisms (Yuan et al., 2024)
[3] Large Language Models are Superpositions of All Characters: Attaining Arbitrary Role-play via Self-Alignment: arxiv.org/abs/2401.1247 [4] Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena: arxiv.org/abs/2306.0568发布于 2024-03-20 17:43・云南 ...
Considerable efforts have been invested in augmenting the role-playing proficiency of open-source large language models (LLMs) by emulating proprietary counterparts. Nevertheless, we posit that LLMs inherently harbor role-play capabilities, owing to the extensive knowledge of characters and potential dial...
Train Models Most hyper parameters are the same as except for the number of steps (the original Humback trains 1600 steps on 512k samples). # change the `--data_path`in`scripts/train_seed.sh` $ bash scripts/train_seed.sh 参考链接: ...
[5] LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion [6] Noise Contrastive Alignment of Language Models with Explicit Rewards 小广告 #Self-Play Preference Optimization for Language Model Alignment# 来自跃问分享 跃问编辑...
This is the official repo for our EMNLP (Main) 2024 paper: Dynamic Rewarding with Prompt Optimization Enables Tuning-free Self-Alignment of Language Models, a novel tuning-free inference-time algorithm to self-align large language models (LLMs) with human preference. Why tuning-free self-alignme...
Abstract: Instruction tuning is a supervised fine-tuning approach that significantly improves the ability of large language models (LLMs) to follow human instructions. We propose SelfCodeAlign, the first fully transparent and permissive pipeline for self-aligning code LLMs without extensive human annota...
"Our self-training approach assumes access to a base language model, a small amount of seed data, and a collection of unlabelled examples, e.g. a web corpus. The unlabelled data is a large, diverse set of human-written documents which includes writing about all manner of topics humans are...
This paper introduces a novel generalized self-imitation learning (GSIL) framework, which effectively and efficiently aligns large language models with offline demonstration data. We developGSILby deriving a surrogate objective of imitation learning with density ratio estimates, facilitating the use of self...
Large language models (LLMs) have attracted significant attention in recommendation systems. Current LLM-based recommender systems primarily rely on supervised fine-tuning (SFT) to train the model for recommendation tasks. However, relying solely on positive samples limits the model's ability to align...