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Found 7 papers, 6 papers with code
Dissecting Human and LLM Preferences
As a relative quality comparison of model responses, human and Large Language Model (LLM) preferences serve as common alignment goals in model fine-tuning and criteria in evaluation.
The Critique of Critique
In this paper, we pioneer the critique of critique, termed MetaCritique, which is a framework to evaluate the critique from two aspects, i. e., factuality as precision score and comprehensiveness as recall score.
Evolving Large Language Model Assistant with Long-Term Conditional Memory
It focuses on preserving the knowledge and experience from the history dialogue between the user and AI assistant, which can be applied to future dialogue for generating a better response.
Generative Judge for Evaluating Alignment
The rapid development of Large Language Models (LLMs) has substantially expanded the range of tasks they can address.
Aligning Language Models with Human Preferences via a Bayesian Approach
For this alignment, current popular methods leverage a reinforcement learning (RL) approach with a reward model trained on feedback from humans.
Improving Sentence Similarity Estimation for Unsupervised Extractive Summarization
Unsupervised extractive summarization aims to extract salient sentences from a document as the summary without labeled data.
Alleviating Exposure Bias via Contrastive Learning for Abstractive Text Summarization
During the training stage, with teacher forcing these models are optimized to maximize the likelihood of the gold summary given the gold summary tokens as input to the decoder, while at inference the given tokens are replaced by the generated tokens.
