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Found 9 papers, 2 papers with code
Evaluating Robustness of Reward Models for Mathematical Reasoning
In this work, we introduce a new design for reliable evaluation of reward models, and to validate this, we construct RewardMATH, a benchmark that effectively represents the robustness of reward models in mathematical reasoning tasks.
Coffee-Gym: An Environment for Evaluating and Improving Natural Language Feedback on Erroneous Code
This paper presents Coffee-Gym, a comprehensive RL environment for training models that provide feedback on code editing.
Large Language Models Are Self-Taught Reasoners: Enhancing LLM Applications via Tailored Problem-Solving Demonstrations
Guiding large language models with a selected set of human-authored demonstrations is a common practice for improving LLM applications.
Towards Lifelong Dialogue Agents via Relation-aware Memory Construction and Timeline-augmented Response Generation
We present Theanine, a framework for LLM-based lifelong dialogue agents.
Language Models as Compilers: Simulating Pseudocode Execution Improves Algorithmic Reasoning in Language Models
Also, we show that compared to natural language, pseudocode can better guide the reasoning of LMs, even though they are trained to follow natural language instructions.
Can Large Language Models be Good Emotional Supporter? Mitigating Preference Bias on Emotional Support Conversation
Motivated by these, we explore the impact of the inherent preference in LLMs on providing emotional support, and consequently, we observe that exhibiting high preference for specific strategies hinders effective emotional support, aggravating its robustness in predicting the appropriate strategy.
Large Language Models are Clinical Reasoners: Reasoning-Aware Diagnosis Framework with Prompt-Generated Rationales
Specifically, we address the clinical reasoning for disease diagnosis, where the LLM generates diagnostic rationales providing its insight on presented patient data and the reasoning path towards the diagnosis, namely Clinical Chain-of-Thought (Clinical CoT).
Coffee: Boost Your Code LLMs by Fixing Bugs with Feedback
Hence, the focus of our work is to leverage open-source code LLMs to generate helpful feedback with correct guidance for code editing.
Dialogue Chain-of-Thought Distillation for Commonsense-aware Conversational Agents
Hence, our focus is to facilitate such multi-hop reasoning over a dialogue context, namely dialogue chain-of-thought (CoT) reasoning.
