-
·
LLaMA-Berry: Pairwise Optimization For O1-Like Olympiad-Level Mathematical Reasoning
The paper titled “LLaMA-Berry: Pairwise Optimization For O1- Like Olympiad-Level Mathematical Reasoning” addresses a critical area in the field of Artificial Intelligence (AI), specifically focusing on enhancing mathematical reasoning capabilities in large language models (LLMs).
-
·
OpenR: An Open Framework For Advanced Reasoning
The paper titled “OpenR: An Open Source Framework For Advanced Reasoning With Large Language Models” addresses a critical aspect of artificial intelligence (AI) by focusing on enhancing the reasoning capabilities of large language models (LLMs).
-
·
CLUs Transform LLMs Into Adaptive Reasoners
The research paper explores the limitations of traditional machine learning models, particularly Large Language Models (LLMs), which often rely on static learning paradigms that require extensive retraining to adapt to new information. The authors introduce Composite Learning Units (CLUs) as a novel framework designed to enhance the adaptability and reasoning capabilities of LLMs through continuous…
-
·
Thinking LLMs: General IF With Thought Generation
This research introduces Thinking LLMs, which enhance traditional Large Language Models (LLMs) by incorporating a mechanism for internal thought generation prior to response generation. The proposed Thought Preference Optimization (TPO) methodology enables these models to improve their instruction-following capabilities without the need for additional human data.
-
·
Understanding the Limitations of Reasoning in LLMs
Let’s distill and learn from: GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models Abstract This document explores the GSM-Symbolic benchmark, a novel framework designed to evaluate the mathematical reasoning capabilities of Large Language Models (LLMs). By addressing the limitations of traditional benchmarks, this framework provides AI engineers with structured methodologies for enhancing…
-
·
Enhancing LLM Capabilities with Tree of Thoughts
The Tree of Thoughts (ToT) framework represents a significant advancement in the capabilities of language models (LMs) for complex problem-solving. By enabling LMs to explore multiple reasoning paths and self-evaluate their decisions, ToT enhances traditional capabilities beyond simple sequential processing. This document provides an in-depth exploration of the ToT framework, its theoretical foundations, algorithm design,…
-
·
Enhance Reasoning By Learning From Mistakes
This document presents an in-depth exploration of the Mistake-Aware Peer-Review Distillation (MAPD) methodology, a novel approach designed to enhance the reasoning capabilities of smaller language models (LMs) through innovative training techniques. By integrating feedback mechanisms that allow models to learn from their mistakes, MAPD offers a significant advancement in knowledge distillation.
Tag: REASONING_LANGUAGE_MODELS
AI models with advanced logical capabilities. Explore techniques enhancing language models for complex problem-solving and inference tasks.