The paper explores the use of AI-powered automated feedback on online tutoring platforms and its impact on tutors.

This paper proposes RLAES, a unified language model framework using reinforcement learning for essay scoring and feedback generation, with methods including Rubric-based Feedback Evaluation (RFE), Ada…

This paper analyzes the use of generative AI for feedback in higher education based on 2988 instances from Estonian bachelor students, finding that students generally find it helpful but not a self-co…

The paper proposes a new dataset and method to leverage implicit user feedback to improve the accuracy of Large Language Models.

This paper proposes a method, LM2LM, to train large language models to generate effective improvement proposals using reinforcement learning and a research world model.

The paper advocates for integrating explicit contextual feedback (like reviews and comments) into LLM-based recommender systems to achieve more personalized, transparent, and semantically aligned reco…

This paper evaluates the ability of Large Language Models to rectify their own code through execution feedback.

The paper presents Tahoe, a system that optimizes Text-to-SQL performance through dynamic data management and hint learning.

This paper proposes a framework to transform historical real-bug mechanisms into executable feedback targets for LLM-based unit test generation, improving real-bug detection over various baselines.

This paper studies how to optimize subscription box services by selecting the most informative items to ask customers about.

This paper proposes conversational AI review assistants for code review, systems that engage in conversation with developers instead of just generating comments.

This paper presents an empirical study on how developers respond to agentic code reviews using CodeRabbit, revealing mixed reception and opportunities for improvement.

The paper introduces PersTurnBench, a novel benchmark and evaluator for assessing personalized user conversation satisfaction at specific turns, addressing the limitation of generic response quality m…

CRITIC-R1 introduces a structured critic framework that treats RAG critique as an explicit error diagnosis problem using reinforcement learning, significantly improving answer quality over strong RAG…

This paper conducted a randomized controlled trial on Reddit to test the effectiveness of various deescalation strategies in reducing personal insults using automated replies.

This paper compares two methods for assessing student teams in tabletop exercises using data from learning platforms and evaluates their validity and reliability.

A system was designed to provide AI-generated response suggestions for social media content creators to maintain their media personas and reduce fatigue.