Search Results for author:
Found 9 papers, 6 papers with code
Rho-1: Not All Tokens Are What You Need
After fine-tuning, Rho-1-1B and 7B achieved state-of-the-art results of 40. 6% and 51. 8% on MATH dataset, respectively - matching DeepSeekMath with only 3% of the pretraining tokens.
Exploring the Mystery of Influential Data for Mathematical Reasoning
Additionally, we showcase the use of QaDS in creating efficient fine-tuning mixtures with various selection ratios, and analyze the quality of a wide range of open-source datasets, which can perform as a reference for future works on mathematical reasoning tasks.
Key-Point-Driven Data Synthesis with its Enhancement on Mathematical Reasoning
Large language models (LLMs) have shown great potential in complex reasoning tasks, yet their performance is often hampered by the scarcity of high-quality and reasoning-focused training datasets.
Ranked #29 on
Math Word Problem Solving
on MATH
CriticBench: Benchmarking LLMs for Critique-Correct Reasoning
Utilizing CriticBench, we evaluate and dissect the performance of 17 LLMs in generation, critique, and correction reasoning, i. e., GQC reasoning.
SciAgent: Tool-augmented Language Models for Scientific Reasoning
To make this task more practical and solvable for LLMs, we introduce a new task setting named tool-augmented scientific reasoning.
ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving
Large language models have made significant progress in various language tasks, yet they still struggle with complex mathematics.
Ranked #10 on
Math Word Problem Solving
on MATH
(using extra training data)
MvP: Multi-view Prompting Improves Aspect Sentiment Tuple Prediction
Generative methods greatly promote aspect-based sentiment analysis via generating a sequence of sentiment elements in a specified format.
Ranked #1 on
Aspect-Based Sentiment Analysis (ABSA)
on ACOS
Aspect-Based Sentiment Analysis
Aspect Category Detection
+10
CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing
Unlike these models, humans typically utilize external tools to cross-check and refine their initial content, like using a search engine for fact-checking, or a code interpreter for debugging.
Long Time No See! Open-Domain Conversation with Long-Term Persona Memory
Most of the open-domain dialogue models tend to perform poorly in the setting of long-term human-bot conversations.
